Introduction

This document contains effect size calculation for the Campbell systematic review ‘Group-based community interventions to support the social reintegration of marginalised adults with mental illness’ (Dalgaard, Flensborg Jensen, Bengtsen, Krassel, & Vembye, 2022).

R package used for effect size calculation

# To retrieved the function used to cluster bias correct effect sizes
# devtools::install_github("MikkelVembye/VIVECampbell")

library(tidyverse)
#library(dplyr)
library(readxl)
library(writexl)
library(purrr)
library(VIVECampbell)
library(metafor)

ICC values used for cluster adjustment

ICC_005 <- 0.05
ICC_01 <- 0.1
ICC_02 <- 0.2

ppcor_imp <- 0.5
R2_imp <- 0

grp_size_imp <- 8

Loading extant data used to calculated population-based effect sizes

# Loading data used to calculate population-based effect size estimates
GAF_string <- if (isTRUE(getOption('knitr.in.progress'))) "GAF_res.rds" else "ES calc/GAF_res.rds"
GAF_pop_res <- readRDS(GAF_string )

PHQ_string <- if (isTRUE(getOption('knitr.in.progress'))) "PHQ_9_res.rds" else "ES calc/PHQ_9_res.rds"
PHQ_pop_res <- readRDS(PHQ_string)

BDI_string <- if (isTRUE(getOption('knitr.in.progress'))) "BDI_res.rds" else "ES calc/BDI_res.rds"
BDI_pop_res <- readRDS(BDI_string)

Studies in meta-analysis

Acarturk et al. (2022) (Quality-checked)

To calculate pretest-adjusted effect sizes, we first estimate pre-post and pre-followup correlations from the reported results in “Treatment effect” section (p. 7)

# _fu = followup

Acarturk_text_res <- 
  tibble(
    
    outcome = rep(c("HSCL-25", "PCL-5", "PSYCHLOPS"), each = 2),
    
    timing = rep(c("Posttest", "Followup"), 3),
    
    N = rep(c(41, 44, 19), c(2, 2, 2)), # Obtained from df(t)/2
    
    m_pre = rep(c(2.34, 1.77, 4.06), each = 2),
    sd_pre = rep(c(0.60, 0.86, 0.76), each = 2),
    
    m_post = c(
      2.15, 2.11, # HSCL-25
      1.43, 1.19, # PCL-5
      3.82, 3.45  # PSYCHLOPS
      
      ),
    
    sd_post = c(
      0.60, 0.59,
      0.79, 0.78,
      0.86, 1.32
      ),
    
    
    paired_t = c(
      2.43, 2.83,
      2.61, 4.45,
      1.01, 2.57
      )
    
  ) |> 
  mutate(
    
    ppcor = ((sd_pre^2*paired_t^2 + sd_post^2*paired_t^2) - (m_post - m_pre)^2 * N)/
      (2*sd_pre*sd_post*paired_t^2),
    
    .by = c(outcome, timing)
    
  ); Acarturk_text_res
## # A tibble: 6 × 9
##   outcome   timing       N m_pre sd_pre m_post sd_post paired_t  ppcor
##   <chr>     <chr>    <dbl> <dbl>  <dbl>  <dbl>   <dbl>    <dbl>  <dbl>
## 1 HSCL-25   Posttest    41  2.34   0.6    2.15    0.6      2.43 0.6519
## 2 HSCL-25   Followup    41  2.34   0.6    2.11    0.59     2.83 0.6176
## 3 PCL-5     Posttest    44  1.77   0.86   1.43    0.79     2.61 0.4541
## 4 PCL-5     Followup    44  1.77   0.86   1.19    0.78     4.45 0.4476
## 5 PSYCHLOPS Posttest    19  4.06   0.76   3.82    0.86     1.01 0.1869
## 6 PSYCHLOPS Followup    19  4.06   0.76   3.45    1.32     2.57 0.6228

Entering data from Table 2 (p. 7)

Acarturk2022 <- 
  tibble(
    
    outcome = rep(c("HSCL-25", "PCL-5", "PSYCHLOPS"), each = 4),
    
    timing = rep(c("Posttest", "Followup"), each = 2, n_distinct(outcome)),
    
    group = rep(c("gPM+", "Control"), n_distinct(outcome)*2),
    
    N = rep(c(24, 22), n_distinct(outcome)*2),
    
    m_pre = c(
        rep(c(2.37, 2.31), 2), # HSCL-25
        rep(c(1.84, 1.70), 2), # PCL-5
        rep(c(4.20, 3.92), 2)  # PSYCHLOPS
        
      ),
    
    sd_pre = c(
        
        rep(c(0.58, 0.64), 2),
        rep(c(0.88, 0.86), 2),
        rep(c(0.68, 0.84), 2)
      
      ),
    
    m_post = c(
      
      2.01, 2.28, 2.07, 2.14, 
      1.27, 1.59, 1.12, 1.26,
      3.82, 3.82, 3.67, 3.23
      
    ),
    
    sd_post = c(
      
      0.59, 0.58, 0.52, 0.43,
      0.70, 0.86, 0.85, 0.70,
      1.00, 0.63, 1.32, 1.63
      
    ),
    
    es_paper = rep(c(
      
      0.48, 0.12,
      0.40, 0.18,
      0, -0.30
      
      ),
      each = 2
    ),
    
    pval_paper = rep(c(
      
      0.109, 0.698,
      0.185, 0.553,
      0.996, 0.319
      
      ),
      each = 2
    )
    
    
    
  ); Acarturk2022
## # A tibble: 12 × 10
##    outcome   timing   group       N m_pre sd_pre m_post sd_post es_paper
##    <chr>     <chr>    <chr>   <dbl> <dbl>  <dbl>  <dbl>   <dbl>    <dbl>
##  1 HSCL-25   Posttest gPM+       24  2.37   0.58   2.01    0.59     0.48
##  2 HSCL-25   Posttest Control    22  2.31   0.64   2.28    0.58     0.48
##  3 HSCL-25   Followup gPM+       24  2.37   0.58   2.07    0.52     0.12
##  4 HSCL-25   Followup Control    22  2.31   0.64   2.14    0.43     0.12
##  5 PCL-5     Posttest gPM+       24  1.84   0.88   1.27    0.7      0.4 
##  6 PCL-5     Posttest Control    22  1.7    0.86   1.59    0.86     0.4 
##  7 PCL-5     Followup gPM+       24  1.84   0.88   1.12    0.85     0.18
##  8 PCL-5     Followup Control    22  1.7    0.86   1.26    0.7      0.18
##  9 PSYCHLOPS Posttest gPM+       24  4.2    0.68   3.82    1        0   
## 10 PSYCHLOPS Posttest Control    22  3.92   0.84   3.82    0.63     0   
## 11 PSYCHLOPS Followup gPM+       24  4.2    0.68   3.67    1.32    -0.3 
## 12 PSYCHLOPS Followup Control    22  3.92   0.84   3.23    1.63    -0.3 
##    pval_paper
##         <dbl>
##  1      0.109
##  2      0.109
##  3      0.698
##  4      0.698
##  5      0.185
##  6      0.185
##  7      0.553
##  8      0.553
##  9      0.996
## 10      0.996
## 11      0.319
## 12      0.319
wide_format_Acarturk2022 <- 
  function(data, filter_value, trt_name, ctr_name){
  
  filter_func <- 
    function(data, filter_value, trt_name, ctr_name){
      
        
    dat <- data |> dplyr::filter(timing == filter_value)
    
    dat |> 
    dplyr::mutate(group = case_match(group, trt_name ~ "t", ctr_name ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    )
    
    
  }
  
  purrr::pmap_dfr(
    list(filter_value), ~ 
    filter_func(data = data, filter_value = .x,
                trt_name = trt_name, ctr_name = ctr_name)) |> 
    arrange(outcome)
  
  
}

Acarturk2022_wide <- 
  wide_format_Acarturk2022(
    data = Acarturk2022, 
    filter_value = c("Posttest", "Followup"),
    trt_name = "gPM+",
    ctr_name = "Control"
  ) |> 
  mutate(
    es_paper = es_paper_t,
    es_paper_type = "d effect size from a mixed-model analysis",
    pval_paper = pval_paper_t
  ) |> 
  select(-c(es_paper_t:pval_paper_c)) 

Effect size calculation and cluster bias adjustment

eta_acarturk_sqrt <- VIVECampbell::eta_1armcluster(
  N_total = 46, Nc = 22, avg_grp_size = 10, ICC = ICC_01,
  sqrt = TRUE
)

Acarturk2022_es <- 
  Acarturk2022 |>
  mutate(
    ppcor = rep(Acarturk_text_res$ppcor, each = 2)
  ) |> 
  summarise(
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Acarturk et al. 2022",
    main_es_method = "Raw diff-in-diffs",
    
    ppcor = ppcor[1],  
    ppcor_method = "Calculated from study",
    
    N_t = N[1],
    N_c = N[2],
    
    N_total = N_t + N_c, 
    
    N_start_t = N_t,
    N_start_c = N_c,
    
    df_ind = N_total,
    
    n_covariates = 1,
    
    es_paper = es_paper[1],
    pval_paper = pval_paper[1],
    tval_paper = qnorm(pval_paper/2, lower.tail = FALSE),
    se_es_paper = es_paper/tval_paper,
    se_es_paper = if_else(se_es_paper == 0, sqrt(sum(1/N)) * eta_acarturk_sqrt, se_es_paper),
    var_es_paper = se_es_paper^2,
    
    sd_pool = sqrt( sum((N-1)*sd_post^2)/ (N_total-2) ),
    m_diff_post = (m_post[1] - m_post[2]) * -1,
    
    # Difference in differences measures
    m_diff_t = (m_post[1] - m_pre[1]) * -1,  
    m_diff_c = (m_post[2] - m_pre[2]) * -1,
    
    DD = m_diff_t - m_diff_c,
    
    d_post = m_diff_post/sd_pool, 
    vd_post = sum(1/N) + d_post^2/(2*df_ind),
    Wd_post = sum(1/N),
    
    d_DD = DD/sd_pool,
    vd_DD = 2*(1-ppcor) * sum(1/N) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * sum(1/N),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = sum(1/N) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * sum(1/N) + g_DD^2/(2*df_ind), 
    Wg_DD = Wd_DD,
    
    .by = c(outcome, timing) 
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # " eight to ten participants per group" (p. 1)
    avg_cl_size = 10, 
    avg_cl_type = "From study (p. 1)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    

    gt_post = g_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = vgt_post:Wgt_post
    )
    
) |> 
ungroup()

Acarturk2022_est <- 
  left_join(Acarturk2022_wide, Acarturk2022_es) |> 
  relocate(study) |> 
  mutate(
    vary_id = paste0(outcome, "/", timing),
    analysis_plan = case_when(
      outcome == "HSCL-25" ~ "All mental health outcomes",
      outcome == "PCL-5" ~ "All mental health outcomes",
      outcome == "PSYCHLOPS" ~ "Wellbeing and Quality of Life",
      TRUE ~ NA_character_  # Fallback in case of unexpected outcomes
    )
  )

Acarturk2022_est
## # A tibble: 6 × 69
##   study                outcome   timing     N_t   N_c m_pre_t m_pre_c sd_pre_t
##   <chr>                <chr>     <chr>    <dbl> <dbl>   <dbl>   <dbl>    <dbl>
## 1 Acarturk et al. 2022 HSCL-25   Posttest    24    22    2.37    2.31     0.58
## 2 Acarturk et al. 2022 HSCL-25   Followup    24    22    2.37    2.31     0.58
## 3 Acarturk et al. 2022 PCL-5     Posttest    24    22    1.84    1.7      0.88
## 4 Acarturk et al. 2022 PCL-5     Followup    24    22    1.84    1.7      0.88
## 5 Acarturk et al. 2022 PSYCHLOPS Posttest    24    22    4.2     3.92     0.68
## 6 Acarturk et al. 2022 PSYCHLOPS Followup    24    22    4.2     3.92     0.68
##   sd_pre_c m_post_t m_post_c sd_post_t sd_post_c es_paper
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl>
## 1     0.64     2.01     2.28      0.59      0.58     0.48
## 2     0.64     2.07     2.14      0.52      0.43     0.12
## 3     0.86     1.27     1.59      0.7       0.86     0.4 
## 4     0.86     1.12     1.26      0.85      0.7      0.18
## 5     0.84     3.82     3.82      1         0.63     0   
## 6     0.84     3.67     3.23      1.32      1.63    -0.3 
##   es_paper_type                             pval_paper effect_size
##   <chr>                                          <dbl> <chr>      
## 1 d effect size from a mixed-model analysis      0.109 SMD        
## 2 d effect size from a mixed-model analysis      0.698 SMD        
## 3 d effect size from a mixed-model analysis      0.185 SMD        
## 4 d effect size from a mixed-model analysis      0.553 SMD        
## 5 d effect size from a mixed-model analysis      0.996 SMD        
## 6 d effect size from a mixed-model analysis      0.319 SMD        
##   sd_used            main_es_method     ppcor ppcor_method          N_total
##   <chr>              <chr>              <dbl> <chr>                   <dbl>
## 1 Pooled posttest SD Raw diff-in-diffs 0.6519 Calculated from study      46
## 2 Pooled posttest SD Raw diff-in-diffs 0.6176 Calculated from study      46
## 3 Pooled posttest SD Raw diff-in-diffs 0.4541 Calculated from study      46
## 4 Pooled posttest SD Raw diff-in-diffs 0.4476 Calculated from study      46
## 5 Pooled posttest SD Raw diff-in-diffs 0.1869 Calculated from study      46
## 6 Pooled posttest SD Raw diff-in-diffs 0.6228 Calculated from study      46
##   N_start_t N_start_c df_ind n_covariates tval_paper se_es_paper var_es_paper
##       <dbl>     <dbl>  <dbl>        <dbl>      <dbl>       <dbl>        <dbl>
## 1        24        22     46            1   1.603         0.2995      0.08970
## 2        24        22     46            1   0.3880        0.3093      0.09564
## 3        24        22     46            1   1.326         0.3018      0.09106
## 4        24        22     46            1   0.5933        0.3034      0.09205
## 5        24        22     46            1   0.005013      0.3465      0.1201 
## 6        24        22     46            1   0.9965       -0.3010      0.09063
##   sd_pool m_diff_post m_diff_t m_diff_c      DD  d_post vd_post Wd_post    d_DD
##     <dbl>       <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1  0.5852     0.27      0.3600  0.03000  0.33    0.4613 0.08943 0.08712  0.5639
## 2  0.4792     0.07000   0.3000  0.17     0.1300  0.1461 0.08735 0.08712  0.2713
## 3  0.7805     0.32      0.57    0.1100   0.46    0.4100 0.08895 0.08712  0.5894
## 4  0.7820     0.1400    0.72    0.44     0.28    0.1790 0.08747 0.08712  0.3581
## 5  0.8439     0         0.3800  0.1000   0.2800  0      0.08712 0.08712  0.3318
## 6  1.476     -0.44      0.53    0.69    -0.1600 -0.2981 0.08809 0.08712 -0.1084
##     vd_DD   Wd_DD      J  g_post vg_post Wg_post    g_DD   vg_DD   Wg_DD
##     <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 0.06412 0.06066 0.9836  0.4538 0.08936 0.08712  0.5546 0.06400 0.06066
## 2 0.06742 0.06662 0.9836  0.1437 0.08735 0.08712  0.2669 0.06740 0.06662
## 3 0.09890 0.09512 0.9836  0.4033 0.08889 0.08712  0.5797 0.09877 0.09512
## 4 0.09764 0.09625 0.9836  0.1761 0.08746 0.08712  0.3522 0.09759 0.09625
## 5 0.1429  0.1417  0.9836  0      0.08712 0.08712  0.3264 0.1428  0.1417 
## 6 0.06585 0.06572 0.9836 -0.2932 0.08806 0.08712 -0.1066 0.06585 0.06572
##   avg_cl_size avg_cl_type         icc icc_type gamma_sqrt df_adj  omega   gt_DD
##         <dbl> <chr>             <dbl> <chr>         <dbl>  <dbl>  <dbl>   <dbl>
## 1          10 From study (p. 1)   0.1 Imputed       0.965  42.49 0.9822  0.5345
## 2          10 From study (p. 1)   0.1 Imputed       0.965  42.49 0.9822  0.2572
## 3          10 From study (p. 1)   0.1 Imputed       0.965  42.49 0.9822  0.5587
## 4          10 From study (p. 1)   0.1 Imputed       0.965  42.49 0.9822  0.3394
## 5          10 From study (p. 1)   0.1 Imputed       0.965  42.49 0.9822  0.3145
## 6          10 From study (p. 1)   0.1 Imputed       0.965  42.49 0.9822 -0.1027
##    vgt_DD  Wgt_DD    hg_DD  vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD
##     <dbl>   <dbl>    <dbl>   <dbl> <dbl> <dbl>           <dbl> <chr>     
## 1 0.08695 0.08359  0.2817  0.02353 42.49 42.49               1 eta       
## 2 0.09259 0.09181  0.1300  0.02353 42.49 42.49               1 eta       
## 3 0.1347  0.1311   0.2356  0.02353 42.49 42.49               1 eta       
## 4 0.1340  0.1326   0.1427  0.02353 42.49 42.49               1 eta       
## 5 0.1964  0.1952   0.1091  0.02353 42.49 42.49               1 eta       
## 6 0.09069 0.09057 -0.05236 0.02353 42.49 42.49               1 eta       
##   adj_value_DD gt_post vgt_post Wgt_post vary_id           
##          <dbl>   <dbl>    <dbl>    <dbl> <chr>             
## 1        1.378  0.4379   0.1223   0.1201 HSCL-25/Posttest  
## 2        1.378  0.1387   0.1203   0.1201 HSCL-25/Followup  
## 3        1.378  0.3892   0.1218   0.1201 PCL-5/Posttest    
## 4        1.378  0.1699   0.1204   0.1201 PCL-5/Followup    
## 5        1.378  0        0.1201   0.1201 PSYCHLOPS/Posttest
## 6        1.378 -0.2829   0.1210   0.1201 PSYCHLOPS/Followup
##   analysis_plan                
##   <chr>                        
## 1 All mental health outcomes   
## 2 All mental health outcomes   
## 3 All mental health outcomes   
## 4 All mental health outcomes   
## 5 Wellbeing and Quality of Life
## 6 Wellbeing and Quality of Life

Barbic et al. (2009) (Quality-checked)

Entering data from Table 3 (p. 495).

Barbic2009 <- tibble(
  outcome = rep(c("Hope Index", "Empowerment Scale", "Quality of Life", "Recovery Assessment"), each = 2),
  group = rep(c("Recovery Workbook", "Control"), dplyr::n_distinct(outcome)),
  N = rep(c(16, 17), dplyr::n_distinct(outcome)),
  
  N_start = rep(c(16,17), 4),
  
  m_pre = c(
    37.13, 36.00, 
    55.93, 63.88, 
    20.34, 20.70, 
    163.75, 156.41),
  
   sd_pre = c(
    6.53, 5.36, 
    6.91, 6.91, 
    5.01, 4.13, 
    22.60, 14.22),
  
  m_post = c(
    38.93, 35.06, 
    14.93, 61.96, # The post-measurement seems flawed from therefore we exclude it from the analysis
    21.60, 22.27, 
    168.81, 149.11),
  
  
  sd_post = c(
    5.34, 6.21, 
    10.43, 7.33, 
    3.35, 4.91, 
    20.11, 22.09)
); Barbic2009
## # A tibble: 8 × 8
##   outcome             group                 N N_start  m_pre sd_pre m_post
##   <chr>               <chr>             <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
## 1 Hope Index          Recovery Workbook    16      16  37.13   6.53  38.93
## 2 Hope Index          Control              17      17  36      5.36  35.06
## 3 Empowerment Scale   Recovery Workbook    16      16  55.93   6.91  14.93
## 4 Empowerment Scale   Control              17      17  63.88   6.91  61.96
## 5 Quality of Life     Recovery Workbook    16      16  20.34   5.01  21.6 
## 6 Quality of Life     Control              17      17  20.7    4.13  22.27
## 7 Recovery Assessment Recovery Workbook    16      16 163.8   22.6  168.8 
## 8 Recovery Assessment Control              17      17 156.4   14.22 149.1 
##   sd_post
##     <dbl>
## 1    5.34
## 2    6.21
## 3   10.43
## 4    7.33
## 5    3.35
## 6    4.91
## 7   20.11
## 8   22.09
Barbic2009_wide <- 
  Barbic2009 |> 
    mutate(group = case_match(group, "Recovery Workbook" ~ "t", "Control" ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    ) |> 
  mutate(
# Based on the degrees of freedom value reported in Barbic et al. (2009),
# we cannot replicate the report F_values. Therefore, we do not use any F_values for 
# for the effect size correlation

  p_val_f = c(.05, .21, .96, .02), 
  df1 = 1, 
  df2 = 31,
  F_val = qf(p_val_f, df1 = df1, df2 = df2, lower.tail = FALSE)
  )

Effect size calculating, including cluster-bias correction

Barbic2009_est <- 
  Barbic2009_wide |>
  mutate(
    analysis_plan = rep(
      c("Hope, Empowerment & Self-efficacy", "Wellbeing and Quality of Life", 
        "Hope, Empowerment & Self-efficacy"), 
      c(2, 1, 1))
  ) |> 
  rowwise() |> 
  mutate(
    
    F_val = if_else(outcome == "Empowerment Scale", 14.93, F_val), # Retrieved from (p. 495)
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Barbic et al. 2009",
    main_es_method = "Raw diff-in-diffs",
    
    ppcor = ppcor_imp,  
    ppcor_method = "Imputed",
    
  #  R2 = NA_real_,
  #  R2_calc_method = "Not relevant",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Calculate d post
    m_post = if_else(outcome != "Empowerment Scale", m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # Account for the fact that some outcomes are on different scales
    m_diff_t = if_else(outcome != "Empowerment Scale", m_post_t - m_pre_t, (m_post_t - m_pre_t) * -1),
    m_diff_c = if_else(outcome != "Empowerment Scale", m_post_c - m_pre_c, (m_post_c - m_pre_c) * -1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    # Average cluster size in treatment group
    # "12 weekly two-hour group sessions with seven to nine participants." (p. 493)
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 493)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    
    vary_id = outcome
    

  ) |> 
  ungroup() |> 
  relocate(N_total, .after = N_c); Barbic2009_est
## # A tibble: 4 × 65
##   outcome               N_t   N_c N_total N_start_t N_start_c m_pre_t m_pre_c
##   <chr>               <dbl> <dbl>   <dbl>     <dbl>     <dbl>   <dbl>   <dbl>
## 1 Hope Index             16    17      33        16        17   37.13   36   
## 2 Empowerment Scale      16    17      33        16        17   55.93   63.88
## 3 Quality of Life        16    17      33        16        17   20.34   20.7 
## 4 Recovery Assessment    16    17      33        16        17  163.8   156.4 
##   sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c p_val_f   df1   df2
##      <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl>   <dbl> <dbl> <dbl>
## 1     6.53     5.36    38.93    35.06      5.34      6.21    0.05     1    31
## 2     6.91     6.91    14.93    61.96     10.43      7.33    0.21     1    31
## 3     5.01     4.13    21.6     22.27      3.35      4.91    0.96     1    31
## 4    22.6     14.22   168.8    149.1      20.11     22.09    0.02     1    31
##       F_val analysis_plan                     effect_size sd_used           
##       <dbl> <chr>                             <chr>       <chr>             
## 1  4.160    Hope, Empowerment & Self-efficacy SMD         Pooled posttest SD
## 2 14.93     Hope, Empowerment & Self-efficacy SMD         Pooled posttest SD
## 3  0.002556 Wellbeing and Quality of Life     SMD         Pooled posttest SD
## 4  6.016    Hope, Empowerment & Self-efficacy SMD         Pooled posttest SD
##   study              main_es_method    ppcor ppcor_method df_ind  m_post sd_pool
##   <chr>              <chr>             <dbl> <chr>         <dbl>   <dbl>   <dbl>
## 1 Barbic et al. 2009 Raw diff-in-diffs   0.5 Imputed          33  3.87     5.805
## 2 Barbic et al. 2009 Raw diff-in-diffs   0.5 Imputed          33 47.03     8.965
## 3 Barbic et al. 2009 Raw diff-in-diffs   0.5 Imputed          33 -0.6700   4.228
## 4 Barbic et al. 2009 Raw diff-in-diffs   0.5 Imputed          33 19.7     21.16 
##    d_post vd_post Wd_post      J  g_post vg_post Wg_post m_diff_t m_diff_c
##     <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1  0.6666  0.1281  0.1213 0.9771  0.6514  0.1278  0.1213    1.800  -0.9400
## 2  5.246   0.5383  0.1213 0.9771  5.126   0.5194  0.1213   41       1.920 
## 3 -0.1585  0.1217  0.1213 0.9771 -0.1549  0.1217  0.1213    1.260   1.57  
## 4  0.9312  0.1345  0.1213 0.9771  0.9099  0.1339  0.1213    5.06   -7.300 
##       d_DD  vd_DD  Wd_DD     g_DD  vg_DD  Wg_DD avg_cl_size avg_cl_type        
##      <dbl>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl>       <dbl> <chr>              
## 1  0.4720  0.1247 0.1213  0.4612  0.1245 0.1213           8 From study (p. 493)
## 2  4.359   0.4092 0.1213  4.259   0.3962 0.1213           8 From study (p. 493)
## 3 -0.07333 0.1214 0.1213 -0.07165 0.1214 0.1213           8 From study (p. 493)
## 4  0.5843  0.1265 0.1213  0.5709  0.1263 0.1213           8 From study (p. 493)
##     icc icc_type n_covariates gamma_sqrt df_adj  omega gt_post vgt_post Wgt_post
##   <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>   <dbl>    <dbl>    <dbl>
## 1   0.1 Imputed             1      0.962  30.29 0.9750  0.6253   0.1656   0.1592
## 2   0.1 Imputed             1      0.962  30.29 0.9750  4.921    0.5589   0.1592
## 3   0.1 Imputed             1      0.962  30.29 0.9750 -0.1487   0.1595   0.1592
## 4   0.1 Imputed             1      0.962  30.29 0.9750  0.8735   0.1718   0.1592
##      gt_DD vgt_DD Wgt_DD    hg_DD  vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD
##      <dbl>  <dbl>  <dbl>    <dbl>   <dbl> <dbl> <dbl>           <dbl> <chr>     
## 1  0.4427  0.1624 0.1592  0.2009  0.03301 30.29 30.29               1 eta       
## 2  4.089   0.4352 0.1592  1.540   0.03301 30.29 30.29               1 eta       
## 3 -0.06878 0.1593 0.1592 -0.03132 0.03301 30.29 30.29               1 eta       
## 4  0.5480  0.1641 0.1592  0.2483  0.03301 30.29 30.29               1 eta       
##   adj_value_DD vary_id            
##          <dbl> <chr>              
## 1        1.312 Hope Index         
## 2        1.312 Empowerment Scale  
## 3        1.312 Quality of Life    
## 4        1.312 Recovery Assessment

Bond et al. (2015) (Quality-checked)

Entering data from Table 3 and 4 (p. 1032). Recovery Assessment Scale Scores are retrieved from the text on page 1032 reported under Table 3. In our analysis, we do not distinguish between different types of work as done in the paper. We, therefore, amalgamate all work categories. We coded the control group as the treatment group because the control is the group-based treatment, which is of our main concern.

# _t = Work choice

Bond2015_continious <- 
  tibble(
    outcome = c("Recovery Assessment Scale", "Days hospitalized"),
    
    N_t = 43,
    N_c = c(42, 41),
    
    N_start_t = 44,
    N_start_c = 43,
    
    m_post_t = c(4.14, 4.93),
    sd_post_t = c(0.49, 7.59),
    m_post_c = c(4.14, 10.44), 
    sd_post_c = c(0.57, 23.07),
    
    analysis_plan = c("Hope, Empowerment & Self-efficacy", "Psychiatric hospitalization"),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Bond et al. 2015",
    main_es_method = "Posttest es",
    
    ppcor_method = "Posttest only",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Calculate d post
    m_post = if_else(outcome != "Days hospitalized", m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post
  ) |> 
  rowwise() |> 
  mutate(
    
    # Cluster bias correction
    # Average cluster size in treatment group
    # "Classes were scheduled weekly at two conveniently located sites" (p. 1029)
    # Assuming two class in the Work Choice group is 44/2 = 22
    avg_cl_size = 22, 
    avg_cl_type = "Guessed from study",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    n_covariates = 0,
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = -c(var_term1_post, n_covariates_post)
      ),
  
    
    vary_id = outcome
    
  )

Bond2015_binary <- 
  tibble(
    outcome = c("Employment", "Risk of Psychiatric Hospitalization"),
    N_t = c(43, 42), # See note a in Table for sample size of Table 4 (i.e., 42, 40, respectively)
    N_c = c(42, 40),
    N_start_t = 44, 
    N_start_c = 43,
    
    A = c(13, 12), 
    B = c(30, 31),  
    C = c(20, 11),
    D = c(22, 30),
    
    analysis_plan = c("Employment", "Psychiatric hospitalization"),
    
    effect_size = "OR",
    sd_used = "Not relevant",
    
    study = "Bond et al. 2015",
    main_es_method = "Raw events",
    
    n_covariates = 0,
    
    N_total = N_t + N_c,
    
    p_t = A/N_t,
    p_c = C/N_c,
    
    RD = p_t - p_c,
    vRD = (A*B)/N_t^3 + (C*D)/N_c^3,
    
    # Average cluster size in treatment group
    # "Classes were scheduled weekly at two conveniently located sites" (p. 1029)
    # Assuming two class in the Work Choice group is 44/2 = 22
    avg_cl_size = 22, 
    avg_cl_type = "Guessed from study",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    # OR calculation and cluster bias adjustment as recommended by Cochrane
    VIVECampbell::OR_calc(
      A = A, B = B, C = C, D = D, 
      ICC = icc, avg_cl_size = avg_cl_size, n_cluster_arms = 1
    ),
    
    vary_id = outcome
    
)

Bond2015_est <- bind_rows(Bond2015_continious, Bond2015_binary)
Bond2015_est
## # A tibble: 4 × 57
## # Rowwise: 
##   outcome                               N_t   N_c N_start_t N_start_c m_post_t
##   <chr>                               <dbl> <dbl>     <dbl>     <dbl>    <dbl>
## 1 Recovery Assessment Scale              43    42        44        43     4.14
## 2 Days hospitalized                      43    41        44        43     4.93
## 3 Employment                             43    42        44        43    NA   
## 4 Risk of Psychiatric Hospitalization    42    40        44        43    NA   
##   sd_post_t m_post_c sd_post_c analysis_plan                     effect_size
##       <dbl>    <dbl>     <dbl> <chr>                             <chr>      
## 1      0.49     4.14      0.57 Hope, Empowerment & Self-efficacy SMD        
## 2      7.59    10.44     23.07 Psychiatric hospitalization       SMD        
## 3     NA       NA        NA    Employment                        OR         
## 4     NA       NA        NA    Psychiatric hospitalization       OR         
##   sd_used            study            main_es_method ppcor_method  N_total
##   <chr>              <chr>            <chr>          <chr>           <dbl>
## 1 Pooled posttest SD Bond et al. 2015 Posttest es    Posttest only      85
## 2 Pooled posttest SD Bond et al. 2015 Posttest es    Posttest only      84
## 3 Not relevant       Bond et al. 2015 Raw events     <NA>               85
## 4 Not relevant       Bond et al. 2015 Raw events     <NA>               82
##   df_ind m_post sd_pool  d_post  vd_post  Wd_post       J  g_post  vg_post
##    <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>
## 1     85   0     0.5310  0       0.04707  0.04707  0.9912  0       0.04707
## 2     84   5.51 17.00    0.3240  0.04827  0.04765  0.9910  0.3211  0.04826
## 3     NA  NA    NA      NA      NA       NA       NA      NA      NA      
## 4     NA  NA    NA      NA      NA       NA       NA      NA      NA      
##    Wg_post avg_cl_size avg_cl_type          icc icc_type gamma_sqrt n_covariates
##      <dbl>       <dbl> <chr>              <dbl> <chr>         <dbl>        <dbl>
## 1  0.04707          22 Guessed from study   0.1 Imputed       0.962            0
## 2  0.04765          22 Guessed from study   0.1 Imputed       0.962            0
## 3 NA                22 Guessed from study   0.1 Imputed      NA                0
## 4 NA                22 Guessed from study   0.1 Imputed      NA                0
##   df_adj   omega gt_post vgt_post Wgt_post hg_post vhg_post h_post df_post
##    <dbl>   <dbl>   <dbl>    <dbl>    <dbl>   <dbl>    <dbl>  <dbl>   <dbl>
## 1  80.12  0.9906  0       0.09352  0.09352  0       0.01282  77.99   77.99
## 2  79.12  0.9905  0.3088  0.09467  0.09405  0.1146  0.01299  77      77   
## 3  NA    NA      NA      NA       NA       NA      NA        NA      NA   
## 4  NA    NA      NA      NA       NA       NA      NA        NA      NA   
##   adj_fct_post adj_value_post vary_id                                 A     B
##   <chr>                 <dbl> <chr>                               <dbl> <dbl>
## 1 eta                   1.987 Recovery Assessment Scale              NA    NA
## 2 eta                   1.974 Days hospitalized                      NA    NA
## 3 <NA>                 NA     Employment                             13    30
## 4 <NA>                 NA     Risk of Psychiatric Hospitalization    12    31
##       C     D     p_t     p_c       RD      vRD      OR    ln_OR  vln_OR     DE
##   <dbl> <dbl>   <dbl>   <dbl>    <dbl>    <dbl>   <dbl>    <dbl>   <dbl>  <dbl>
## 1    NA    NA NA      NA      NA       NA       NA      NA       NA      NA    
## 2    NA    NA NA      NA      NA       NA       NA      NA       NA      NA    
## 3    20    22  0.3023  0.4762 -0.1739   0.01084  0.4767 -0.7409   0.2057  1.987
## 4    11    30  0.2857  0.275   0.01071  0.01018  1.056   0.05422  0.2398  1.974
##   vln_OR_C
##      <dbl>
## 1  NA     
## 2  NA     
## 3   0.4088
## 4   0.4734

Bækkelund et al. (2022) (Quality-checked)

Entering data from Table 5 (p. 10)

Bækkelund2022 <- tibble(
  group = rep(c("Intervention", #Intervention group
                "Control"),     #Control group
              each = 1, 4),
  
  
  outcome = rep(c("GAF-S",     # Global Assesment of Function - Split version
                  "SCL-90 R"),  # Symptom Checklist 90 Revised
                each = 2, 2), 
  
  
  timing = rep(c("post", "6m"), each = 4), 
  
  N = rep(c(
    29, 30, 
    29, 30), 
    each = 1, 2), 

  N_start = N,
  
  m_pre = rep(c(
    41.7, 40.9,
    1.8, 1.9),
    each = 1,2), 
  
  
  sd_pre = rep(c(
    5.7, 8.2,
    0.7, 0.9), 
    each = 1,2), 
  
  m_post = c(
    # 6 months
    43.9, 44.2,
    1.7, 1.9,
    
    # 36 months
    48.4, 45.7,
    1.7, 1.9),
  
  sd_post = c(
    # 6 months
    6.3, 8.7,
    0.8, 0.9,
    
    # 36 months
    6.3, 4.2,
    0.9, 1.0),
  
  #_rm = repeated measure
  d_rm = c(
    0.4, 0.4, 
    0.1, .0,
    1.1, 0.7,
    0.1, .0 # Was reported as 0.4 but we can infer from the Table that this might have been a reporting mistake 
    
  ),
  
  # To calculated the pre-posttest correlation, we need the standard deviation of
  # the mean differences (sd_diff). As can be seen from page 6. We can obtain
  # sd_diff from the reported within-group effect sizes as follows
  sd_diff = abs(m_post - m_pre)/d_rm,
  
  # The group individual pre-posttest correlation can be obtained as follows.
  # Note that we are not able to obtained correlations from d_rm = 0. This only 
  # concerns SCL90 pre-posttest effect sizes for the control group. Hereto, 
  # 
  r = (sd_pre^2 + sd_post^2 - sd_diff^2)/(2*sd_pre*sd_post)
  
  
  ) |> 
  mutate(
    # Here we assume that the pre-post correlation is equal among groups
    r = if_else(is.na(r), mean(r, na.rm = TRUE), r), 
    
    .by = c(outcome, timing)

  ) |> 
  mutate(
    # To estiamte the average pre-posttest correlation
    z = 0.5 * log( (1+r)/(1-r) ),
    v = 1/(N-3),
    w = 1/v
    
  ); Bækkelund2022
## # A tibble: 8 × 15
##   group        outcome  timing     N N_start m_pre sd_pre m_post sd_post  d_rm
##   <chr>        <chr>    <chr>  <dbl>   <dbl> <dbl>  <dbl>  <dbl>   <dbl> <dbl>
## 1 Intervention GAF-S    post      29      29  41.7    5.7   43.9     6.3   0.4
## 2 Control      GAF-S    post      30      30  40.9    8.2   44.2     8.7   0.4
## 3 Intervention SCL-90 R post      29      29   1.8    0.7    1.7     0.8   0.1
## 4 Control      SCL-90 R post      30      30   1.9    0.9    1.9     0.9   0  
## 5 Intervention GAF-S    6m        29      29  41.7    5.7   48.4     6.3   1.1
## 6 Control      GAF-S    6m        30      30  40.9    8.2   45.7     4.2   0.7
## 7 Intervention SCL-90 R 6m        29      29   1.8    0.7    1.7     0.9   0.1
## 8 Control      SCL-90 R 6m        30      30   1.9    0.9    1.9     1     0  
##   sd_diff      r      z       v     w
##     <dbl>  <dbl>  <dbl>   <dbl> <dbl>
## 1   5.500 0.5838 0.6682 0.03846    26
## 2   8.250 0.5247 0.5828 0.03704    27
## 3   1.000 0.1161 0.1166 0.03846    26
## 4 NaN     0.1161 0.1166 0.03704    27
## 5   6.091 0.4885 0.5340 0.03846    26
## 6   6.857 0.5496 0.6179 0.03704    27
## 7   1.000 0.2381 0.2428 0.03846    26
## 8 NaN     0.2381 0.2428 0.03704    27
# Making the tibble into wideformat in order to estimate effect sizes
Bækkelund2022_wide <-
  Bækkelund2022 |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  ) 


# Effect size calculating 
Bækkelund2022_est <-           
  Bækkelund2022_wide |>
  mutate(
    
    study = "Bækkelund et al. 2022",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    analysis_plan = case_when(
      str_detect(outcome, "GAF-S") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "SCL-90 R") ~ "All mental health outcomes"),
    
    main_es_method = "Raw diff-in-diffs",
    
    mean_z =  (w_t*z_t + w_c*z_c)/(w_t + w_c),  
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "Calculated from study results",
    
    m_diff_t = m_post_t - m_pre_t, 
    m_diff_c = m_post_c - m_pre_c,
    # SCL-90 R lower scores are beneficial why these is reverted
    mean_diff = if_else(outcome != "GAF-S", (m_diff_t - m_diff_c)*-1,
                        m_diff_t - m_diff_c),
    
    
    # SCL-90 R lower scores are beneficial why these is reverted
    m_post = if_else(outcome != "GAF-S", (m_post_t - m_post_c)*-1,
                     m_post_t - m_post_c),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    N_total = N_t + N_c,
    df_ind = N_total, 
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind), 
    Wd_post = (1/N_t + 1/N_c), 
    
    d_DD = (mean_diff/sd_pool), 
    vd_DD = (1/N_t + 1/N_c) * 2*(1-ppcor) + d_DD^2/(2*df_ind), 
    Wd_DD = (1/N_t + 1/N_c) * 2*(1-ppcor), 
    
    J = 1 - 3/(4*df_ind-1), 
    
    g_post = J * (m_post/sd_pool),
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = (1/N_t + 1/N_c),
  
    g_DD = J * (mean_diff/sd_pool), 
    vg_DD = (1/N_t + 1/N_c) * 2*(1-ppcor) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD,
    
    .by = outcome
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # "each group was led by two therapists and had nine participants." (p. 4)
    avg_cl_size = 9,
    avg_cl_type = "Obtained from article (p. 4)",
    
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1, 
    
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc, sqrt = TRUE
    ),
    
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * ((m_diff_t - m_diff_c)/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste(outcome, timing, sep = "/")
    
  ); Bækkelund2022_est
## # A tibble: 4 × 79
## # Rowwise: 
##   outcome  timing   N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t
##   <chr>    <chr>  <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>
## 1 GAF-S    post      29    30        29        30    41.7    40.9      5.7
## 2 SCL-90 R post      29    30        29        30     1.8     1.9      0.7
## 3 GAF-S    6m        29    30        29        30    41.7    40.9      5.7
## 4 SCL-90 R 6m        29    30        29        30     1.8     1.9      0.7
##   sd_pre_c m_post_t m_post_c sd_post_t sd_post_c d_rm_t d_rm_c sd_diff_t
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl>  <dbl>  <dbl>     <dbl>
## 1      8.2     43.9     44.2       6.3       8.7    0.4    0.4     5.500
## 2      0.9      1.7      1.9       0.8       0.9    0.1    0       1.000
## 3      8.2     48.4     45.7       6.3       4.2    1.1    0.7     6.091
## 4      0.9      1.7      1.9       0.9       1      0.1    0       1.000
##   sd_diff_c    r_t    r_c    z_t    z_c     v_t     v_c   w_t   w_c
##       <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl> <dbl> <dbl>
## 1     8.250 0.5838 0.5247 0.6682 0.5828 0.03846 0.03704    26    27
## 2   NaN     0.1161 0.1161 0.1166 0.1166 0.03846 0.03704    26    27
## 3     6.857 0.4885 0.5496 0.5340 0.6179 0.03846 0.03704    26    27
## 4   NaN     0.2381 0.2381 0.2428 0.2428 0.03846 0.03704    26    27
##   study                 effect_size sd_used           
##   <chr>                 <chr>       <chr>             
## 1 Bækkelund et al. 2022 SMD         Pooled posttest SD
## 2 Bækkelund et al. 2022 SMD         Pooled posttest SD
## 3 Bækkelund et al. 2022 SMD         Pooled posttest SD
## 4 Bækkelund et al. 2022 SMD         Pooled posttest SD
##   analysis_plan                             main_es_method    mean_z  ppcor
##   <chr>                                     <chr>              <dbl>  <dbl>
## 1 Social functioning (degree of impairment) Raw diff-in-diffs 0.6247 0.5544
## 2 All mental health outcomes                Raw diff-in-diffs 0.1166 0.1161
## 3 Social functioning (degree of impairment) Raw diff-in-diffs 0.5767 0.5203
## 4 All mental health outcomes                Raw diff-in-diffs 0.2428 0.2381
##   ppcor_method                  m_diff_t m_diff_c mean_diff  m_post sd_pool
##   <chr>                            <dbl>    <dbl>     <dbl>   <dbl>   <dbl>
## 1 Calculated from study results   2.200     3.300   -1.100  -0.3000  7.616 
## 2 Calculated from study results  -0.1000    0        0.1000  0.2     0.8523
## 3 Calculated from study results   6.700     4.800    1.900   2.700   5.336 
## 4 Calculated from study results  -0.1000    0        0.1000  0.2     0.9522
##   N_total df_ind   d_post vd_post Wd_post    d_DD   vd_DD   Wd_DD      J
##     <dbl>  <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>
## 1      59     59 -0.03939 0.06783 0.06782 -0.1444 0.06061 0.06044 0.9872
## 2      59     59  0.2346  0.06828 0.06782  0.1173 0.1200  0.1199  0.9872
## 3      59     59  0.5060  0.06999 0.06782  0.3561 0.06614 0.06506 0.9872
## 4      59     59  0.2100  0.06819 0.06782  0.1050 0.1034  0.1033  0.9872
##     g_post vg_post Wg_post    g_DD   vg_DD   Wg_DD avg_cl_size
##      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>       <dbl>
## 1 -0.03889 0.06783 0.06782 -0.1426 0.06061 0.06044           9
## 2  0.2317  0.06827 0.06782  0.1158 0.1200  0.1199            9
## 3  0.4995  0.06993 0.06782  0.3515 0.06611 0.06506           9
## 4  0.2074  0.06818 0.06782  0.1037 0.1034  0.1033            9
##   avg_cl_type                    icc icc_type n_covariates gamma_sqrt df_adj
##   <chr>                        <dbl> <chr>           <dbl>      <dbl>  <dbl>
## 1 Obtained from article (p. 4)   0.1 Imputed             1      0.967  55.09
## 2 Obtained from article (p. 4)   0.1 Imputed             1      0.967  55.09
## 3 Obtained from article (p. 4)   0.1 Imputed             1      0.967  55.09
## 4 Obtained from article (p. 4)   0.1 Imputed             1      0.967  55.09
##    omega  gt_post vgt_post Wgt_post   gt_DD  vgt_DD  Wgt_DD    hg_DD  vhg_DD
##    <dbl>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>    <dbl>   <dbl>
## 1 0.9863 -0.03757  0.09211  0.09209 -0.1378 0.08224 0.08207 -0.06476 0.01815
## 2 0.9863  0.2238   0.09255  0.09209  0.1119 0.1629  0.1628   0.03736 0.01815
## 3 0.9863  0.4826   0.09421  0.09209  0.3396 0.08940 0.08836  0.1536  0.01815
## 4 0.9863  0.2003   0.09246  0.09209  0.1002 0.1404  0.1403   0.03602 0.01815
##    h_DD df_DD n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop
##   <dbl> <dbl>           <dbl> <chr>             <dbl>     <dbl>      <dbl>
## 1 55.09 55.09               1 eta               1.358  -0.08238    0.07845
## 2 55.09 55.09               1 eta               1.358  NA         NA      
## 3 55.09 55.09               1 eta               1.358   0.1423     0.08449
## 4 55.09 55.09               1 eta               1.358  NA         NA      
##   Wgt_DD_pop vary_id      
##        <dbl> <chr>        
## 1    0.07844 GAF-S/post   
## 2   NA       SCL-90 R/post
## 3    0.08444 GAF-S/6m     
## 4   NA       SCL-90 R/6m

Cano-Vindel et al. (2021) (Quality-checked)

Data entered from Tables 2 and 3 (pp. 3342-45)

Cano_vindel2021 <- tibble(
  analysis = rep(c("ITT", "Per-protocol"), each = 80),
  group = rep(c("TAU", "TAU-GCBT"), each = 1,80),
  
  outcome = rep(c("GAD-7", "PHQ-9", "PHQ15", "working_life_Function",
                  "Socal_life_Function", "Family_life_Function", "Physical_QoL",
                  "Psychological_QoL", "Social_QoL", "Environment_QoL"), 
                each = 8,2),
  
  timing = rep(c("Post", "3m", "6m", "12m"), each = 2,20),
  
  N = c(rep(c(534, 527), 40), # ITT
        rep(c(316, 315, # per-protocol post-treatment
              238, 273,# per-protocol 3 months
              204, 229,# per-protocol 6 months
              180, 208), 1,80) # per-protocol 12 months
  ),
  
  N_start = rep(c(534,527), each = 80),
  
  m_pre = c(
    rep(c(12.1, 12.5), each = 1,4),  # ITT GAD-7 Anxiety Baseline)
    rep(c(13.5, 13.7), each = 1,4),  # ITT PHQ9
    rep(c(14, 14.3), each = 1,4), # ITT PHQ15
    rep(c(3.5, 3.6), each = 1,4), # ITT working life
    rep(c(4.6, 4.7), each = 1,4), # ITT social life
    rep(c(4.6, 4.8), each = 1,4), # ITT family life
    rep(c(22.4, 22.1), each = 1,4), # ITT Physical
    rep(c(16.9, 16.9), each = 1,4), # ITT Psychological
    rep(c(9.1, 9.1), each = 1,4), # ITT Social
    rep(c(25.3, 25.7), each = 1,4),  # ITT Environment 
      
    rep(c(12.1, 12.5), each = 1,4), # per-protocol GAD-7 Anxiety Baseline)
    rep(c(13.5, 13.7), each = 1,4), # per-protocol GAD-7 PHQ9
    rep(c(14, 14.3), each = 1,4),   # per-protocol GAD-7 PHQ15
    rep(c(3.5, 3.6), each = 1,4),   # per-protocol GAD-7 working life
    rep(c(4.6, 4.7), each = 1,4),   # per-protocol GAD-7 social life
    rep(c(4.6, 4.8), each = 1,4),   # per-protocol GAD-7 family life
    rep(c(22.4, 22.1), each = 1,4), # per-protocol GAD-7 Physical
    rep(c(16.9, 16.9), each = 1,4), # per-protocol GAD-7 Psychological
    rep(c(9.1, 9.1), each = 1,4),   # per-protocol GAD-7 Social
    rep(c(25.3, 25.7), each = 1,4)  # per-protocol GAD-7 Environment 
  ), 
  
  sd_pre = c(
    rep(c(4.7, 4.6), each = 1,4),  # ITT GAD-7 Standard deviation
    rep(c(5.4, 5.3), each = 1,4), # ITT PHQ9
    rep(c(4.8, 4.9),each = 1,4), # ITT PHQ15
    rep(c(3.1, 3.2), each = 1,4), # ITT working life
    rep(c(3, 3), each = 1,4), # ITT social life
    rep(c(3.1, 3), each = 1,4), # ITT family life
    rep(c(4.3, 4.3), each = 1,4), # ITT Physical
    rep(c(3.8, 3.8), each = 1,4), # ITT Psychological
    rep(c(2.4, 2.4), each = 1,4), # ITT Social
    rep(c(4.5, 4.6), each = 1,4),  # ITT Environment 
    
    rep(c(4.7, 4.6), each = 1,4),  # per-protocol GAD-7 Standard deviation
    rep(c(5.4, 5.3), each = 1,4), # per-protocol PHQ9
    rep(c(4.8, 4.9),each = 1,4), # per-protocol PHQ15
    rep(c(3.1, 3.2), each = 1,4), # per-protocol working life
    rep(c(3, 3), each = 1,4), # per-protocol social life
    rep(c(3.1, 3), each = 1,4), # per-protocol family life
    rep(c(4.3, 4.3), each = 1,4), # per-protocol Physical
    rep(c(3.8, 3.8), each = 1,4), # per-protocol Psychological
    rep(c(2.4, 2.4), each = 1,4), # per-protocol Social
    rep(c(4.5, 4.6), each = 1,4)  # per-protocol Environment 
      
    
  ), 
  m_post = c(
    9.5, 6.8, # ITT GAD-7 Anxiety post-treatment
    8.7, 7.3, # ITT GAD-7 Anxiety 3 months
    8.6, 6.9, # ITT GAD-7 Anxiety 6 months
    8.3, 6.6,  # ITT GAD-7 Anxiety 12 months
    
    10.8, 8.0, # ITT PHQ9 Depression post treatment
    10.2, 8.4, # ITT PHQ9 Depression 3 months
    9.8, 7.9, # ITT PHQ9 Depression 6 months
    9.4, 7.8, # ITT PHQ9 Depression 12 months
    
    11.7, 9.9, # ITT PHQ-15 post-treatment
    11.4, 10.1,# ITT PHQ-15 3 months
    11.1, 9.8, # ITT PHQ-15 6 months
    10.7, 9.4, # ITT PHQ-15 12 months
    
    3.0, 2.6, # ITT working life post-treatment
    2.7, 2.4, # ITT Working life 3 months 
    2.7, 2.1, # ITT working life 6 months
    3.1, 2.4, # ITT working life 12 months
    
    
    4.1, 3.2, # ITT social life post-treatment
    3.5, 3.2, # ITT social life 3 months 
    3.4, 2.7, # ITT social life 6 months
    3.8, 2.9, # ITT social life 12 months
    
    3.9, 3.1, # ITT Family life post-treatment
    3.5, 3.1, # ITT Family life 3 months 
    3.6, 2.7, # ITT Family life 6 months
    3.8, 2.8, # ITT Family life  12 months
    
    23.2, 24.7, # ITT Physical post-treatment
    23.5, 24.2, # ITT Physical 3 months
    23.6, 24.3, # ITT Physical 6 months
    24.2, 26.4, # ITT Physical12 months
    
    17.7, 19.2, # ITT Psychological post-treatment
    18.1, 18.9, # ITT Psychological 3 months
    18.5, 19.1, # ITT Psychological 6 months
    18.7, 20.8, # ITT Psychological 12 months
    
    9.4, 9.8, # ITT Social post-treatment
    9.5, 9.7, # ITT Social 3 months
    9.5, 9.7, # ITT Social 6 months
    9.7, 11.1, # ITT Social 12 months
    
    25.7, 27.2, # ITT Environment post-treatment 
    26.1, 26.9, # ITT Environment 3 months
    26.5, 27.1, # ITT Environment 6 months
    27.5, 31.2,  # ITT Environment 12 months
    
    10.2, 6.0, # Per-protocol GAD-7 Anxiety post treatment
    8.9, 6.7, # Per-protocol GAD-7 Anxiety 3 months
    8.8, 6.2, # Per-protocol GAD-7 Anxiety  6 months
    8.7, 5.8, # Per-protocol GAD-7 Anxiety 12 months 
    
    11.5, 7.0,  # Per-protocol PHQ9 Depression post-treatment
    10.3, 7.8, # Per protocol PHQ9 Depression 3 months
    10.0, 7.3,  # Per-protocol PHQ9 Depression 6 months
    9.7, 7.1, # Per-protocol PHQ9 Depression 12 months
    
    12.1, 9.1, # Per-protocol PHQ-15 post treatment
    11.7, 9.5, # Per protocol PHQ-15 3 months
    11.5, 9.2, # Per-protocol PHQ-15 6 months
    11.7, 8.8, # Per-protocol PHQ-15 12 months
    
    3.1, 2.4, # Per-protocol working life post treatment
    2.6, 2.5, # Per protocol Working life 3 months
    2.8, 1.9, # Per-protocol working life 6 months
    3.3, 2.0, # Per-protocol working life 12 months
    
    
    4.1, 2.9, # Per-protocol social life post treatment
    3.4, 3.1, # Per protocol social life 3 months
    3.6, 2.6, # Per-protocol social life 6 months
    4.0, 2.6, # Per-protocol social life 12 months
    
    4.0, 2.8, # Per-protocol Family life post treatment
    3.5, 3.0, # Per protocol Family life 3 months
    3.6, 2.6, # Per-protocol Family life 6 months
    3.9, 2.5, # Per-protocol Family life 12 months
    
    
    22.7, 25.1,# Per-protocol Physical post treatment
    23.2, 24.4, # Per protocol Physical 3 months
    23.1, 24.7, # Per-protocol Physical 6 months 
    22.7, 25.6, # Per-protocol Physical 12 months
    
    17.4, 19.9, # Per-protocol Psychological post treatment
    18.0, 19.3, # Per protocol Psychological 3 months
    18.3, 19.3, # Per-protocol Psychological 6 months
    18.3, 20.2, # Per-protocol Psychological 12 months
    
    9.2, 10.0, # Per-protocol Social post treatment
    9.3, 9.8, # Per protocol Social 3 months
    9.6, 9.8, # Per-protocol Social 6 months
    9.3, 10.0, # Per-protocol Social 12 months
    
    25.5, 27.8, # Per-protocol Environment post treatment
    26.1, 27.5, # Per protocol Environment 3 months
    26.4, 27.7, # Per-protocol Environment 6 months
    26.6, 28.3) # Per-protocol Environment 12 months
    ,
  
  sd_post = c(
    5.4, 4.7, # ITT GAD-7 Anxiety post-treatment
    5.3, 5.0, # ITT GAD-7 Standard deviation 3 months
    5.4, 5.1, # ITT GAD-7 Standard deviation 6 months
    5.7, 5.4,  # ITT GAD-7 Standard deviation 12 months
    
    6.4, 5.7, # ITT PHQ9 Depression standard deviation post-treatment 
    6.4, 6.0, # ITT PHQ9 Depression standard deviation 3 months
    6.4, 6.1, # ITT PHQ9 Depression standard deviation 6 months
    6.3, 5.9, # ITT PHQ9 Depression standard deviation 12 months
    
    5.2, 5.4, # ITT PHQ-15 standard deviation post-treatment
    5.1, 5.3, # ITT PHQ-15 standard deviation 3 months
    5.3, 5.6, # ITT PHQ-15 standard deviation 6 months
    5.6, 5.6, # ITT PHQ-15 standard deviation 12 months
    
    3.1, 3.0, # ITT working life standard deviation post-treatment
    3.0, 3.0, # ITT working life standard deviation 3 months
    3.0, 2.9, # ITT working life standard deviation 6 months
    3.3, 3.2, # ITT working life standard deviation 12 months
    
    3.1, 3.0, # ITT social life standard deviation post-treatment
    3.1, 2.9, # ITT social life standard deviation 3 months
    3.2, 3.1, # ITT social life standard deviation 6 months
    3.4, 3.4, # ITT social life standard deviation 12 months
    
    3.1, 2.9, # ITT Family life standard deviation post-treatment
    3.1, 3.1, # ITT Family life standard deviation 3 months
    3.2, 3.1, # ITT Family life standard deviation 6 months
    3.3, 3.2, # ITT Family life standard deviation 12 months
    
    4.5, 4.6, # ITT Physical standard deviation post-treatment
    4.6, 4.8, # ITT Physical standard deviation 3 months
    4.5, 4.4, # ITT Physical standard deviation 6 months
    5.1, 5.3, # ITT Physical standard deviation 12 months
    
    3.9, 4.0, # ITT Psychological standard deviation post-treatment
    3.9, 4.2, # ITT Psychological standard deviation 3 months
    3.8, 3.9, # ITT Psychological standard deviation 6 months
    4.4, 4.6, # ITT Psychological standard deviation 12 months
    
    3.1, 3.3, # ITT Social standard deviation post-treatment
    2.3, 2.3, # ITT Social standard deviation 3 months
    2.5, 2.2, # ITT Social standard deviation 6 months
    2.2, 2.6, # ITT Social standard deviation 12 months
    
    5.3, 5.6, # ITT Environment standard deviation post-treatment
    4.9, 5.1, # ITT Environment standard deviation 3 months
    4.8, 4.8, # ITT Environment standard deviation 6 months
    5.0, 5.3, # ITT Environment standard deviation 12 months
    
    5.5, 4.3, # Per-protocol GAD-7 Anxiety standard deviation post-treatment
    5.4, 4.9, # Per-protocol GAD-7 Anxiety standard deviation 3 months
    5.7, 4.9, # Per-protocol GAD-7 Anxiety standard deviation 6 months
    5.8, 5.3, # Per-protocol GAD-7 Anxiety standard deviation 12 months
    
    6.6, 5.2, # Per-protocol PHQ9 Depression standard deviation post-treatment
    6.5, 6.0, # Per-protocol PHQ9 Depression standard deviation 3 months
    6.6, 6.1, # Per-protocol PHQ9 Depression standard deviation 6 months
    6.5, 6.2, # Per-protocol PHQ9 Depression standard deviation 12 months
    
    5.2, 5.3, # Per-protocol PHQ-15 standard deviation post-treatment
    5.0, 5.4, # Per-protocol PHQ-15 standard deviation 3 months
    5.3, 5.7, # Per-protocol PHQ-15 standard deviation 6 months
    5.6, 5.7, # Per-protocol PHQ-15 standard deviation 12 months
    
    3.1, 2.9, # Per-protocol working life standard deviation post-treatment
    3.0, 2.9, # Per-protocol working life standard deviation 3 months
    3.0, 2.7, # Per-protocol working life standard deviation 6 months
    3.3, 2.7, # Per-protocol working life standard deviation 12 months
    
    3.1, 2.8, # Per-protocol social life standard deviation post-treatment
    3.2, 2.9, # Per-protocol social life standard deviation 3 months
    3.2, 2.8, # Per-protocol social life standard deviation 6 months
    3.3, 3.1, # Per-protocol social life standard deviation 12 months
    
    3.1, 3.1, # Per-protocol Family life standard deviation post-treatment
    3.1, 3.0, # Per-protocol Family life standard deviation 3 months
    3.1, 2.7, # Per-protocol Family life standard deviation 6 months
    3.3, 2.8,  # Per-protocol Family life standard deviation 12 months
    
    4.6, 4.7, # Per-protocol Physical standard deviation post-treatment
    4.8, 4.9, # Per-protocol Physical standard deviation 3 months
    4.8, 4.9, # Per-protocol Physical standard deviation 6 months
    5.1, 5.3, # Per-protocol Physical standard deviation 12 months
    
    4.2, 4.0, # Per-protocol Psychological standard deviation post-treatment
    4.0, 4.2, # Per-protocol Psychological standard deviation 3 months
    4.2, 4.2, # Per-protocol Psychological standard deviation 6 months
    4.4, 4.6, # Per-protocol Psychological standard deviation 12 months
    
    2.4, 2.7, # Per-protocol Social standard deviation post-treatment
    2.2, 2.4, # Per-protocol Social standard deviation 3 months
    2.5, 2.2, # Per-protocol Social standard deviation 6 months
    2.2, 2.6, # Per-protocol Social standard deviation 12 months
    
    4.7, 4.8, # Per-protocol Environment standard deviation post-treatment
    4.8, 5.2, # Per-protocol Environment standard deviation 3 months
    4.8, 5.0, # Per-protocol Environment standard deviation 6 months
    5.0, 5.3) # Per-protocol Environment standard deviation 12 months
  
)

Since we could not backout any pre-posttests correlation estiamtes, we used test-retest reliability measures from the given scale literature (Arbuckle et al., 2009; Ilić et al., 2019; Ravesteijn et al., 2009; Spitzer, Kroenke, Williams, & Löwe, 2006; Zuithoff et al., 2010) as recommended by Hedges et al (2023).

test_retest_cano2021 <- 
  tibble(
    # Repeat 8 times to make it fit the es data (see below)
    outcome = unique(Cano_vindel2021$outcome), 
    tr_reliability = c(
      0.83, # From Spitzer et al. 2006
      0.94, # From Zuithoff et al. 2010
      0.60, # From Ravesteijn et al. 2009
      
      0.63, # From Arbuckle et al. 2009
      0.70, # From Arbuckle et al. 2009
      0.61, # From Arbuckle et al. 2009
      
      0.665, # Ilíc et al. 2019
      0.724, # Ilíc et al. 2019
      0.725, # Ilíc et al. 2019
      0.60   # Ilíc et al. 2019
      )
    
  ); test_retest_cano2021 
## # A tibble: 10 × 2
##    outcome               tr_reliability
##    <chr>                          <dbl>
##  1 GAD-7                          0.83 
##  2 PHQ-9                          0.94 
##  3 PHQ15                          0.6  
##  4 working_life_Function          0.63 
##  5 Socal_life_Function            0.7  
##  6 Family_life_Function           0.61 
##  7 Physical_QoL                   0.665
##  8 Psychological_QoL              0.724
##  9 Social_QoL                     0.725
## 10 Environment_QoL                0.6

Effect size calculation, including cluster bias correction

# Turning data into wide format
params <- tibble(
  filter_val1 = rep(unique(Cano_vindel2021$analysis), each = 4),
  filter_val2 = rep(c("Post", paste0(c(3, 6, 12), "m")), 2)
)

wide_cano2021_func <- 
  function(filter_val1, filter_val2){
  
    Cano_vindel2021 |> 
    filter(analysis == filter_val1 & timing == filter_val2) |> 
    mutate(group = case_match(group, "TAU-GCBT" ~ "t", "TAU" ~ "c")) |>
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    )   
      
  }
 
Cano_vindel2021_est <- 
  pmap(params, wide_cano2021_func) |> 
  list_rbind() |> 
  left_join(test_retest_cano2021) |> 
  rename(ppcor = tr_reliability) |> 
  mutate(
    analysis_plan = case_when(
      outcome == "GAD-7" ~ "All mental health outcomes/Anxiety",
      outcome == "PHQ-9" ~ "All mental health outcomes/Depression",
      outcome == "PHQ15" ~ "All mental health outcomes",
      str_detect(outcome, "Funct") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "QoL") ~ "Wellbeing and Quality of Life",
      TRUE ~ NA_character_
    )
  ) |>
  relocate(analysis_plan) |> 
  rowwise() |> 
  mutate(
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Cano-Vindel et al. 2021",
    main_es_method = "Raw diff-in-diffs",
    
    ppcor_method = "Based on test-retest reliability estimates",
    
    N_total = N_t + N_c,
    df_ind = N_total,
     
    # Calculate d post
    m_post = if_else(str_detect(outcome, "QoL"), m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
   
    # Account for the fact that some outcomes are on different scales
    m_diff_t = if_else(str_detect(outcome, "QoL"), m_post_t - m_pre_t, (m_post_t - m_pre_t) * -1),
    m_diff_c = if_else(str_detect(outcome, "QoL"), m_post_c - m_pre_c, (m_post_c - m_pre_c) * -1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    # Average cluster size in treatment group
    # "held over a 12–14-week period in small groups (8–10 patients) in the primary care centre" (p. 3338)
    avg_cl_size = 9, 
    avg_cl_type = "From study (p. 3338)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),

    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    

    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "9"),
      omega * ((m_diff_t - m_diff_c)/PHQ_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*PHQ_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste(analysis, outcome, timing, sep = "/")
    
  ) |> 
  ungroup(); Cano_vindel2021_est 
## # A tibble: 80 × 66
##    analysis_plan                             analysis     outcome              
##    <chr>                                     <chr>        <chr>                
##  1 All mental health outcomes/Anxiety        ITT          GAD-7                
##  2 All mental health outcomes/Depression     ITT          PHQ-9                
##  3 All mental health outcomes                ITT          PHQ15                
##  4 Social functioning (degree of impairment) ITT          working_life_Function
##  5 Social functioning (degree of impairment) ITT          Socal_life_Function  
##  6 Social functioning (degree of impairment) ITT          Family_life_Function 
##  7 Wellbeing and Quality of Life             ITT          Physical_QoL         
##  8 Wellbeing and Quality of Life             ITT          Psychological_QoL    
##  9 Wellbeing and Quality of Life             ITT          Social_QoL           
## 10 Wellbeing and Quality of Life             ITT          Environment_QoL      
## 11 All mental health outcomes/Anxiety        ITT          GAD-7                
## 12 All mental health outcomes/Depression     ITT          PHQ-9                
## 13 All mental health outcomes                ITT          PHQ15                
## 14 Social functioning (degree of impairment) ITT          working_life_Function
## 15 Social functioning (degree of impairment) ITT          Socal_life_Function  
## 16 Social functioning (degree of impairment) ITT          Family_life_Function 
## 17 Wellbeing and Quality of Life             ITT          Physical_QoL         
## 18 Wellbeing and Quality of Life             ITT          Psychological_QoL    
## 19 Wellbeing and Quality of Life             ITT          Social_QoL           
## 20 Wellbeing and Quality of Life             ITT          Environment_QoL      
## 21 All mental health outcomes/Anxiety        ITT          GAD-7                
## 22 All mental health outcomes/Depression     ITT          PHQ-9                
## 23 All mental health outcomes                ITT          PHQ15                
## 24 Social functioning (degree of impairment) ITT          working_life_Function
## 25 Social functioning (degree of impairment) ITT          Socal_life_Function  
## 26 Social functioning (degree of impairment) ITT          Family_life_Function 
## 27 Wellbeing and Quality of Life             ITT          Physical_QoL         
## 28 Wellbeing and Quality of Life             ITT          Psychological_QoL    
## 29 Wellbeing and Quality of Life             ITT          Social_QoL           
## 30 Wellbeing and Quality of Life             ITT          Environment_QoL      
## 31 All mental health outcomes/Anxiety        ITT          GAD-7                
## 32 All mental health outcomes/Depression     ITT          PHQ-9                
## 33 All mental health outcomes                ITT          PHQ15                
## 34 Social functioning (degree of impairment) ITT          working_life_Function
## 35 Social functioning (degree of impairment) ITT          Socal_life_Function  
## 36 Social functioning (degree of impairment) ITT          Family_life_Function 
## 37 Wellbeing and Quality of Life             ITT          Physical_QoL         
## 38 Wellbeing and Quality of Life             ITT          Psychological_QoL    
## 39 Wellbeing and Quality of Life             ITT          Social_QoL           
## 40 Wellbeing and Quality of Life             ITT          Environment_QoL      
## 41 All mental health outcomes/Anxiety        Per-protocol GAD-7                
## 42 All mental health outcomes/Depression     Per-protocol PHQ-9                
## 43 All mental health outcomes                Per-protocol PHQ15                
## 44 Social functioning (degree of impairment) Per-protocol working_life_Function
## 45 Social functioning (degree of impairment) Per-protocol Socal_life_Function  
## 46 Social functioning (degree of impairment) Per-protocol Family_life_Function 
## 47 Wellbeing and Quality of Life             Per-protocol Physical_QoL         
## 48 Wellbeing and Quality of Life             Per-protocol Psychological_QoL    
## 49 Wellbeing and Quality of Life             Per-protocol Social_QoL           
## 50 Wellbeing and Quality of Life             Per-protocol Environment_QoL      
## 51 All mental health outcomes/Anxiety        Per-protocol GAD-7                
## 52 All mental health outcomes/Depression     Per-protocol PHQ-9                
## 53 All mental health outcomes                Per-protocol PHQ15                
## 54 Social functioning (degree of impairment) Per-protocol working_life_Function
## 55 Social functioning (degree of impairment) Per-protocol Socal_life_Function  
## 56 Social functioning (degree of impairment) Per-protocol Family_life_Function 
## 57 Wellbeing and Quality of Life             Per-protocol Physical_QoL         
## 58 Wellbeing and Quality of Life             Per-protocol Psychological_QoL    
## 59 Wellbeing and Quality of Life             Per-protocol Social_QoL           
## 60 Wellbeing and Quality of Life             Per-protocol Environment_QoL      
## 61 All mental health outcomes/Anxiety        Per-protocol GAD-7                
## 62 All mental health outcomes/Depression     Per-protocol PHQ-9                
## 63 All mental health outcomes                Per-protocol PHQ15                
## 64 Social functioning (degree of impairment) Per-protocol working_life_Function
## 65 Social functioning (degree of impairment) Per-protocol Socal_life_Function  
## 66 Social functioning (degree of impairment) Per-protocol Family_life_Function 
## 67 Wellbeing and Quality of Life             Per-protocol Physical_QoL         
## 68 Wellbeing and Quality of Life             Per-protocol Psychological_QoL    
## 69 Wellbeing and Quality of Life             Per-protocol Social_QoL           
## 70 Wellbeing and Quality of Life             Per-protocol Environment_QoL      
## 71 All mental health outcomes/Anxiety        Per-protocol GAD-7                
## 72 All mental health outcomes/Depression     Per-protocol PHQ-9                
## 73 All mental health outcomes                Per-protocol PHQ15                
## 74 Social functioning (degree of impairment) Per-protocol working_life_Function
## 75 Social functioning (degree of impairment) Per-protocol Socal_life_Function  
## 76 Social functioning (degree of impairment) Per-protocol Family_life_Function 
## 77 Wellbeing and Quality of Life             Per-protocol Physical_QoL         
## 78 Wellbeing and Quality of Life             Per-protocol Psychological_QoL    
## 79 Wellbeing and Quality of Life             Per-protocol Social_QoL           
## 80 Wellbeing and Quality of Life             Per-protocol Environment_QoL      
##    timing   N_c   N_t N_start_c N_start_t m_pre_c m_pre_t sd_pre_c sd_pre_t
##    <chr>  <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
##  1 Post     534   527       534       534    12.1    12.5      4.7      4.6
##  2 Post     534   527       534       534    13.5    13.7      5.4      5.3
##  3 Post     534   527       534       534    14      14.3      4.8      4.9
##  4 Post     534   527       534       534     3.5     3.6      3.1      3.2
##  5 Post     534   527       534       534     4.6     4.7      3        3  
##  6 Post     534   527       534       534     4.6     4.8      3.1      3  
##  7 Post     534   527       534       534    22.4    22.1      4.3      4.3
##  8 Post     534   527       534       534    16.9    16.9      3.8      3.8
##  9 Post     534   527       534       534     9.1     9.1      2.4      2.4
## 10 Post     534   527       534       534    25.3    25.7      4.5      4.6
## 11 3m       534   527       534       534    12.1    12.5      4.7      4.6
## 12 3m       534   527       534       534    13.5    13.7      5.4      5.3
## 13 3m       534   527       534       534    14      14.3      4.8      4.9
## 14 3m       534   527       534       534     3.5     3.6      3.1      3.2
## 15 3m       534   527       534       534     4.6     4.7      3        3  
## 16 3m       534   527       534       534     4.6     4.8      3.1      3  
## 17 3m       534   527       534       534    22.4    22.1      4.3      4.3
## 18 3m       534   527       534       534    16.9    16.9      3.8      3.8
## 19 3m       534   527       534       534     9.1     9.1      2.4      2.4
## 20 3m       534   527       534       534    25.3    25.7      4.5      4.6
## 21 6m       534   527       534       534    12.1    12.5      4.7      4.6
## 22 6m       534   527       534       534    13.5    13.7      5.4      5.3
## 23 6m       534   527       534       534    14      14.3      4.8      4.9
## 24 6m       534   527       534       534     3.5     3.6      3.1      3.2
## 25 6m       534   527       534       534     4.6     4.7      3        3  
## 26 6m       534   527       534       534     4.6     4.8      3.1      3  
## 27 6m       534   527       534       534    22.4    22.1      4.3      4.3
## 28 6m       534   527       534       534    16.9    16.9      3.8      3.8
## 29 6m       534   527       534       534     9.1     9.1      2.4      2.4
## 30 6m       534   527       534       534    25.3    25.7      4.5      4.6
## 31 12m      534   527       534       534    12.1    12.5      4.7      4.6
## 32 12m      534   527       534       534    13.5    13.7      5.4      5.3
## 33 12m      534   527       534       534    14      14.3      4.8      4.9
## 34 12m      534   527       534       534     3.5     3.6      3.1      3.2
## 35 12m      534   527       534       534     4.6     4.7      3        3  
## 36 12m      534   527       534       534     4.6     4.8      3.1      3  
## 37 12m      534   527       534       534    22.4    22.1      4.3      4.3
## 38 12m      534   527       534       534    16.9    16.9      3.8      3.8
## 39 12m      534   527       534       534     9.1     9.1      2.4      2.4
## 40 12m      534   527       534       534    25.3    25.7      4.5      4.6
## 41 Post     316   315       527       527    12.1    12.5      4.7      4.6
## 42 Post     316   315       527       527    13.5    13.7      5.4      5.3
## 43 Post     316   315       527       527    14      14.3      4.8      4.9
## 44 Post     316   315       527       527     3.5     3.6      3.1      3.2
## 45 Post     316   315       527       527     4.6     4.7      3        3  
## 46 Post     316   315       527       527     4.6     4.8      3.1      3  
## 47 Post     316   315       527       527    22.4    22.1      4.3      4.3
## 48 Post     316   315       527       527    16.9    16.9      3.8      3.8
## 49 Post     316   315       527       527     9.1     9.1      2.4      2.4
## 50 Post     316   315       527       527    25.3    25.7      4.5      4.6
## 51 3m       238   273       527       527    12.1    12.5      4.7      4.6
## 52 3m       238   273       527       527    13.5    13.7      5.4      5.3
## 53 3m       238   273       527       527    14      14.3      4.8      4.9
## 54 3m       238   273       527       527     3.5     3.6      3.1      3.2
## 55 3m       238   273       527       527     4.6     4.7      3        3  
## 56 3m       238   273       527       527     4.6     4.8      3.1      3  
## 57 3m       238   273       527       527    22.4    22.1      4.3      4.3
## 58 3m       238   273       527       527    16.9    16.9      3.8      3.8
## 59 3m       238   273       527       527     9.1     9.1      2.4      2.4
## 60 3m       238   273       527       527    25.3    25.7      4.5      4.6
## 61 6m       204   229       527       527    12.1    12.5      4.7      4.6
## 62 6m       204   229       527       527    13.5    13.7      5.4      5.3
## 63 6m       204   229       527       527    14      14.3      4.8      4.9
## 64 6m       204   229       527       527     3.5     3.6      3.1      3.2
## 65 6m       204   229       527       527     4.6     4.7      3        3  
## 66 6m       204   229       527       527     4.6     4.8      3.1      3  
## 67 6m       204   229       527       527    22.4    22.1      4.3      4.3
## 68 6m       204   229       527       527    16.9    16.9      3.8      3.8
## 69 6m       204   229       527       527     9.1     9.1      2.4      2.4
## 70 6m       204   229       527       527    25.3    25.7      4.5      4.6
## 71 12m      180   208       527       527    12.1    12.5      4.7      4.6
## 72 12m      180   208       527       527    13.5    13.7      5.4      5.3
## 73 12m      180   208       527       527    14      14.3      4.8      4.9
## 74 12m      180   208       527       527     3.5     3.6      3.1      3.2
## 75 12m      180   208       527       527     4.6     4.7      3        3  
## 76 12m      180   208       527       527     4.6     4.8      3.1      3  
## 77 12m      180   208       527       527    22.4    22.1      4.3      4.3
## 78 12m      180   208       527       527    16.9    16.9      3.8      3.8
## 79 12m      180   208       527       527     9.1     9.1      2.4      2.4
## 80 12m      180   208       527       527    25.3    25.7      4.5      4.6
##    m_post_c m_post_t sd_post_c sd_post_t ppcor effect_size sd_used           
##       <dbl>    <dbl>     <dbl>     <dbl> <dbl> <chr>       <chr>             
##  1      9.5      6.8       5.4       4.7 0.83  SMD         Pooled posttest SD
##  2     10.8      8         6.4       5.7 0.94  SMD         Pooled posttest SD
##  3     11.7      9.9       5.2       5.4 0.6   SMD         Pooled posttest SD
##  4      3        2.6       3.1       3   0.63  SMD         Pooled posttest SD
##  5      4.1      3.2       3.1       3   0.7   SMD         Pooled posttest SD
##  6      3.9      3.1       3.1       2.9 0.61  SMD         Pooled posttest SD
##  7     23.2     24.7       4.5       4.6 0.665 SMD         Pooled posttest SD
##  8     17.7     19.2       3.9       4   0.724 SMD         Pooled posttest SD
##  9      9.4      9.8       3.1       3.3 0.725 SMD         Pooled posttest SD
## 10     25.7     27.2       5.3       5.6 0.6   SMD         Pooled posttest SD
## 11      8.7      7.3       5.3       5   0.83  SMD         Pooled posttest SD
## 12     10.2      8.4       6.4       6   0.94  SMD         Pooled posttest SD
## 13     11.4     10.1       5.1       5.3 0.6   SMD         Pooled posttest SD
## 14      2.7      2.4       3         3   0.63  SMD         Pooled posttest SD
## 15      3.5      3.2       3.1       2.9 0.7   SMD         Pooled posttest SD
## 16      3.5      3.1       3.1       3.1 0.61  SMD         Pooled posttest SD
## 17     23.5     24.2       4.6       4.8 0.665 SMD         Pooled posttest SD
## 18     18.1     18.9       3.9       4.2 0.724 SMD         Pooled posttest SD
## 19      9.5      9.7       2.3       2.3 0.725 SMD         Pooled posttest SD
## 20     26.1     26.9       4.9       5.1 0.6   SMD         Pooled posttest SD
## 21      8.6      6.9       5.4       5.1 0.83  SMD         Pooled posttest SD
## 22      9.8      7.9       6.4       6.1 0.94  SMD         Pooled posttest SD
## 23     11.1      9.8       5.3       5.6 0.6   SMD         Pooled posttest SD
## 24      2.7      2.1       3         2.9 0.63  SMD         Pooled posttest SD
## 25      3.4      2.7       3.2       3.1 0.7   SMD         Pooled posttest SD
## 26      3.6      2.7       3.2       3.1 0.61  SMD         Pooled posttest SD
## 27     23.6     24.3       4.5       4.4 0.665 SMD         Pooled posttest SD
## 28     18.5     19.1       3.8       3.9 0.724 SMD         Pooled posttest SD
## 29      9.5      9.7       2.5       2.2 0.725 SMD         Pooled posttest SD
## 30     26.5     27.1       4.8       4.8 0.6   SMD         Pooled posttest SD
## 31      8.3      6.6       5.7       5.4 0.83  SMD         Pooled posttest SD
## 32      9.4      7.8       6.3       5.9 0.94  SMD         Pooled posttest SD
## 33     10.7      9.4       5.6       5.6 0.6   SMD         Pooled posttest SD
## 34      3.1      2.4       3.3       3.2 0.63  SMD         Pooled posttest SD
## 35      3.8      2.9       3.4       3.4 0.7   SMD         Pooled posttest SD
## 36      3.8      2.8       3.3       3.2 0.61  SMD         Pooled posttest SD
## 37     24.2     26.4       5.1       5.3 0.665 SMD         Pooled posttest SD
## 38     18.7     20.8       4.4       4.6 0.724 SMD         Pooled posttest SD
## 39      9.7     11.1       2.2       2.6 0.725 SMD         Pooled posttest SD
## 40     27.5     31.2       5         5.3 0.6   SMD         Pooled posttest SD
## 41     10.2      6         5.5       4.3 0.83  SMD         Pooled posttest SD
## 42     11.5      7         6.6       5.2 0.94  SMD         Pooled posttest SD
## 43     12.1      9.1       5.2       5.3 0.6   SMD         Pooled posttest SD
## 44      3.1      2.4       3.1       2.9 0.63  SMD         Pooled posttest SD
## 45      4.1      2.9       3.1       2.8 0.7   SMD         Pooled posttest SD
## 46      4        2.8       3.1       3.1 0.61  SMD         Pooled posttest SD
## 47     22.7     25.1       4.6       4.7 0.665 SMD         Pooled posttest SD
## 48     17.4     19.9       4.2       4   0.724 SMD         Pooled posttest SD
## 49      9.2     10         2.4       2.7 0.725 SMD         Pooled posttest SD
## 50     25.5     27.8       4.7       4.8 0.6   SMD         Pooled posttest SD
## 51      8.9      6.7       5.4       4.9 0.83  SMD         Pooled posttest SD
## 52     10.3      7.8       6.5       6   0.94  SMD         Pooled posttest SD
## 53     11.7      9.5       5         5.4 0.6   SMD         Pooled posttest SD
## 54      2.6      2.5       3         2.9 0.63  SMD         Pooled posttest SD
## 55      3.4      3.1       3.2       2.9 0.7   SMD         Pooled posttest SD
## 56      3.5      3         3.1       3   0.61  SMD         Pooled posttest SD
## 57     23.2     24.4       4.8       4.9 0.665 SMD         Pooled posttest SD
## 58     18       19.3       4         4.2 0.724 SMD         Pooled posttest SD
## 59      9.3      9.8       2.2       2.4 0.725 SMD         Pooled posttest SD
## 60     26.1     27.5       4.8       5.2 0.6   SMD         Pooled posttest SD
## 61      8.8      6.2       5.7       4.9 0.83  SMD         Pooled posttest SD
## 62     10        7.3       6.6       6.1 0.94  SMD         Pooled posttest SD
## 63     11.5      9.2       5.3       5.7 0.6   SMD         Pooled posttest SD
## 64      2.8      1.9       3         2.7 0.63  SMD         Pooled posttest SD
## 65      3.6      2.6       3.2       2.8 0.7   SMD         Pooled posttest SD
## 66      3.6      2.6       3.1       2.7 0.61  SMD         Pooled posttest SD
## 67     23.1     24.7       4.8       4.9 0.665 SMD         Pooled posttest SD
## 68     18.3     19.3       4.2       4.2 0.724 SMD         Pooled posttest SD
## 69      9.6      9.8       2.5       2.2 0.725 SMD         Pooled posttest SD
## 70     26.4     27.7       4.8       5   0.6   SMD         Pooled posttest SD
## 71      8.7      5.8       5.8       5.3 0.83  SMD         Pooled posttest SD
## 72      9.7      7.1       6.5       6.2 0.94  SMD         Pooled posttest SD
## 73     11.7      8.8       5.6       5.7 0.6   SMD         Pooled posttest SD
## 74      3.3      2         3.3       2.7 0.63  SMD         Pooled posttest SD
## 75      4        2.6       3.3       3.1 0.7   SMD         Pooled posttest SD
## 76      3.9      2.5       3.3       2.8 0.61  SMD         Pooled posttest SD
## 77     22.7     25.6       5.1       5.3 0.665 SMD         Pooled posttest SD
## 78     18.3     20.2       4.4       4.6 0.724 SMD         Pooled posttest SD
## 79      9.3     10         2.2       2.6 0.725 SMD         Pooled posttest SD
## 80     26.6     28.3       5         5.3 0.6   SMD         Pooled posttest SD
##    study                   main_es_method   
##    <chr>                   <chr>            
##  1 Cano-Vindel et al. 2021 Raw diff-in-diffs
##  2 Cano-Vindel et al. 2021 Raw diff-in-diffs
##  3 Cano-Vindel et al. 2021 Raw diff-in-diffs
##  4 Cano-Vindel et al. 2021 Raw diff-in-diffs
##  5 Cano-Vindel et al. 2021 Raw diff-in-diffs
##  6 Cano-Vindel et al. 2021 Raw diff-in-diffs
##  7 Cano-Vindel et al. 2021 Raw diff-in-diffs
##  8 Cano-Vindel et al. 2021 Raw diff-in-diffs
##  9 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 10 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 11 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 12 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 13 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 14 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 15 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 16 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 17 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 18 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 19 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 20 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 21 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 22 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 23 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 24 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 25 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 26 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 27 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 28 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 29 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 30 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 31 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 32 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 33 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 34 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 35 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 36 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 37 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 38 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 39 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 40 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 41 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 42 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 43 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 44 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 45 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 46 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 47 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 48 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 49 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 50 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 51 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 52 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 53 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 54 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 55 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 56 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 57 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 58 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 59 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 60 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 61 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 62 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 63 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 64 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 65 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 66 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 67 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 68 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 69 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 70 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 71 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 72 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 73 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 74 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 75 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 76 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 77 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 78 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 79 Cano-Vindel et al. 2021 Raw diff-in-diffs
## 80 Cano-Vindel et al. 2021 Raw diff-in-diffs
##    ppcor_method                               N_total df_ind m_post sd_pool
##    <chr>                                        <dbl>  <dbl>  <dbl>   <dbl>
##  1 Based on test-retest reliability estimates    1061   1061 2.7      5.064
##  2 Based on test-retest reliability estimates    1061   1061 2.8      6.062
##  3 Based on test-retest reliability estimates    1061   1061 1.800    5.300
##  4 Based on test-retest reliability estimates    1061   1061 0.4      3.051
##  5 Based on test-retest reliability estimates    1061   1061 0.9000   3.051
##  6 Based on test-retest reliability estimates    1061   1061 0.8      3.002
##  7 Based on test-retest reliability estimates    1061   1061 1.5      4.550
##  8 Based on test-retest reliability estimates    1061   1061 1.5      3.950
##  9 Based on test-retest reliability estimates    1061   1061 0.4000   3.201
## 10 Based on test-retest reliability estimates    1061   1061 1.5      5.451
## 11 Based on test-retest reliability estimates    1061   1061 1.4      5.153
## 12 Based on test-retest reliability estimates    1061   1061 1.800    6.205
## 13 Based on test-retest reliability estimates    1061   1061 1.3      5.200
## 14 Based on test-retest reliability estimates    1061   1061 0.3000   3    
## 15 Based on test-retest reliability estimates    1061   1061 0.3000   3.002
## 16 Based on test-retest reliability estimates    1061   1061 0.4      3.1  
## 17 Based on test-retest reliability estimates    1061   1061 0.7000   4.700
## 18 Based on test-retest reliability estimates    1061   1061 0.8000   4.052
## 19 Based on test-retest reliability estimates    1061   1061 0.2000   2.3  
## 20 Based on test-retest reliability estimates    1061   1061 0.8000   5.000
## 21 Based on test-retest reliability estimates    1061   1061 1.7      5.253
## 22 Based on test-retest reliability estimates    1061   1061 1.9      6.253
## 23 Based on test-retest reliability estimates    1061   1061 1.300    5.451
## 24 Based on test-retest reliability estimates    1061   1061 0.6      2.951
## 25 Based on test-retest reliability estimates    1061   1061 0.7      3.151
## 26 Based on test-retest reliability estimates    1061   1061 0.9      3.151
## 27 Based on test-retest reliability estimates    1061   1061 0.7000   4.451
## 28 Based on test-retest reliability estimates    1061   1061 0.6000   3.850
## 29 Based on test-retest reliability estimates    1061   1061 0.2000   2.356
## 30 Based on test-retest reliability estimates    1061   1061 0.6000   4.8  
## 31 Based on test-retest reliability estimates    1061   1061 1.700    5.553
## 32 Based on test-retest reliability estimates    1061   1061 1.6      6.105
## 33 Based on test-retest reliability estimates    1061   1061 1.300    5.6  
## 34 Based on test-retest reliability estimates    1061   1061 0.7      3.251
## 35 Based on test-retest reliability estimates    1061   1061 0.9      3.4  
## 36 Based on test-retest reliability estimates    1061   1061 1        3.251
## 37 Based on test-retest reliability estimates    1061   1061 2.2      5.200
## 38 Based on test-retest reliability estimates    1061   1061 2.100    4.500
## 39 Based on test-retest reliability estimates    1061   1061 1.4      2.407
## 40 Based on test-retest reliability estimates    1061   1061 3.7      5.151
## 41 Based on test-retest reliability estimates     631    631 4.2      4.938
## 42 Based on test-retest reliability estimates     631    631 4.5      5.942
## 43 Based on test-retest reliability estimates     631    631 3        5.250
## 44 Based on test-retest reliability estimates     631    631 0.7      3.002
## 45 Based on test-retest reliability estimates     631    631 1.2      2.954
## 46 Based on test-retest reliability estimates     631    631 1.2      3.1  
## 47 Based on test-retest reliability estimates     631    631 2.400    4.650
## 48 Based on test-retest reliability estimates     631    631 2.5      4.101
## 49 Based on test-retest reliability estimates     631    631 0.8000   2.554
## 50 Based on test-retest reliability estimates     631    631 2.3      4.750
## 51 Based on test-retest reliability estimates     511    511 2.2      5.139
## 52 Based on test-retest reliability estimates     511    511 2.5      6.238
## 53 Based on test-retest reliability estimates     511    511 2.2      5.218
## 54 Based on test-retest reliability estimates     511    511 0.1000   2.947
## 55 Based on test-retest reliability estimates     511    511 0.3000   3.043
## 56 Based on test-retest reliability estimates     511    511 0.5      3.047
## 57 Based on test-retest reliability estimates     511    511 1.200    4.854
## 58 Based on test-retest reliability estimates     511    511 1.3      4.108
## 59 Based on test-retest reliability estimates     511    511 0.5      2.309
## 60 Based on test-retest reliability estimates     511    511 1.400    5.018
## 61 Based on test-retest reliability estimates     433    433 2.6      5.292
## 62 Based on test-retest reliability estimates     433    433 2.7      6.340
## 63 Based on test-retest reliability estimates     433    433 2.3      5.515
## 64 Based on test-retest reliability estimates     433    433 0.9      2.845
## 65 Based on test-retest reliability estimates     433    433 1        2.995
## 66 Based on test-retest reliability estimates     433    433 1        2.895
## 67 Based on test-retest reliability estimates     433    433 1.600    4.853
## 68 Based on test-retest reliability estimates     433    433 1        4.2  
## 69 Based on test-retest reliability estimates     433    433 0.2000   2.346
## 70 Based on test-retest reliability estimates     433    433 1.3      4.907
## 71 Based on test-retest reliability estimates     388    388 2.9      5.537
## 72 Based on test-retest reliability estimates     388    388 2.6      6.341
## 73 Based on test-retest reliability estimates     388    388 2.9      5.654
## 74 Based on test-retest reliability estimates     388    388 1.3      2.993
## 75 Based on test-retest reliability estimates     388    388 1.4      3.194
## 76 Based on test-retest reliability estimates     388    388 1.4      3.042
## 77 Based on test-retest reliability estimates     388    388 2.900    5.208
## 78 Based on test-retest reliability estimates     388    388 1.900    4.508
## 79 Based on test-retest reliability estimates     388    388 0.7000   2.423
## 80 Based on test-retest reliability estimates     388    388 1.7      5.163
##     d_post  vd_post  Wd_post      J  g_post  vg_post  Wg_post m_diff_t m_diff_c
##      <dbl>    <dbl>    <dbl>  <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
##  1 0.5331  0.003904 0.003770 0.9993 0.5328  0.003904 0.003770   5.7      2.6   
##  2 0.4619  0.003871 0.003770 0.9993 0.4615  0.003871 0.003770   5.7      2.7   
##  3 0.3396  0.003825 0.003770 0.9993 0.3394  0.003824 0.003770   4.4      2.3   
##  4 0.1311  0.003778 0.003770 0.9993 0.1310  0.003778 0.003770   1        0.5   
##  5 0.2950  0.003811 0.003770 0.9993 0.2948  0.003811 0.003770   1.5      0.5   
##  6 0.2665  0.003804 0.003770 0.9993 0.2663  0.003804 0.003770   1.7      0.7   
##  7 0.3297  0.003821 0.003770 0.9993 0.3294  0.003821 0.003770   2.600    0.8000
##  8 0.3797  0.003838 0.003770 0.9993 0.3795  0.003838 0.003770   2.3      0.8000
##  9 0.1250  0.003778 0.003770 0.9993 0.1249  0.003778 0.003770   0.7000   0.3000
## 10 0.2752  0.003806 0.003770 0.9993 0.2750  0.003806 0.003770   1.5      0.4000
## 11 0.2717  0.003805 0.003770 0.9993 0.2715  0.003805 0.003770   5.2      3.4   
## 12 0.2901  0.003810 0.003770 0.9993 0.2899  0.003810 0.003770   5.3      3.3   
## 13 0.2500  0.003800 0.003770 0.9993 0.2498  0.003800 0.003770   4.2      2.6   
## 14 0.1000  0.003775 0.003770 0.9993 0.09993 0.003775 0.003770   1.2      0.8   
## 15 0.09992 0.003775 0.003770 0.9993 0.09985 0.003775 0.003770   1.5      1.1   
## 16 0.1290  0.003778 0.003770 0.9993 0.1289  0.003778 0.003770   1.7      1.1   
## 17 0.1489  0.003781 0.003770 0.9993 0.1488  0.003781 0.003770   2.100    1.100 
## 18 0.1974  0.003789 0.003770 0.9993 0.1973  0.003789 0.003770   2        1.200 
## 19 0.08696 0.003774 0.003770 0.9993 0.08690 0.003774 0.003770   0.6000   0.4000
## 20 0.1600  0.003782 0.003770 0.9993 0.1599  0.003782 0.003770   1.200    0.8000
## 21 0.3236  0.003820 0.003770 0.9993 0.3234  0.003819 0.003770   5.6      3.5   
## 22 0.3039  0.003814 0.003770 0.9993 0.3036  0.003814 0.003770   5.8      3.7   
## 23 0.2385  0.003797 0.003770 0.9993 0.2383  0.003797 0.003770   4.5      2.9   
## 24 0.2033  0.003790 0.003770 0.9993 0.2032  0.003790 0.003770   1.5      0.8   
## 25 0.2222  0.003793 0.003770 0.9993 0.2220  0.003793 0.003770   2        1.2   
## 26 0.2856  0.003809 0.003770 0.9993 0.2854  0.003809 0.003770   2.1      1     
## 27 0.1573  0.003782 0.003770 0.9993 0.1572  0.003782 0.003770   2.2      1.200 
## 28 0.1558  0.003782 0.003770 0.9993 0.1557  0.003782 0.003770   2.200    1.600 
## 29 0.08490 0.003774 0.003770 0.9993 0.08484 0.003774 0.003770   0.6000   0.4000
## 30 0.1250  0.003778 0.003770 0.9993 0.1249  0.003778 0.003770   1.400    1.200 
## 31 0.3061  0.003814 0.003770 0.9993 0.3059  0.003814 0.003770   5.9      3.8   
## 32 0.2621  0.003803 0.003770 0.9993 0.2619  0.003803 0.003770   5.9      4.1   
## 33 0.2321  0.003796 0.003770 0.9993 0.2320  0.003796 0.003770   4.9      3.3   
## 34 0.2153  0.003792 0.003770 0.9993 0.2152  0.003792 0.003770   1.2      0.4   
## 35 0.2647  0.003803 0.003770 0.9993 0.2645  0.003803 0.003770   1.8      0.8   
## 36 0.3076  0.003815 0.003770 0.9993 0.3074  0.003815 0.003770   2        0.8   
## 37 0.4231  0.003855 0.003770 0.9993 0.4228  0.003854 0.003770   4.300    1.8   
## 38 0.4666  0.003873 0.003770 0.9993 0.4663  0.003873 0.003770   3.90     1.8   
## 39 0.5816  0.003930 0.003770 0.9993 0.5812  0.003929 0.003770   2        0.6000
## 40 0.7183  0.004013 0.003770 0.9993 0.7178  0.004013 0.003770   5.5      2.2   
## 41 0.8506  0.006913 0.006339 0.9988 0.8496  0.006911 0.006339   6.5      1.9   
## 42 0.7573  0.006794 0.006339 0.9988 0.7564  0.006792 0.006339   6.7      2     
## 43 0.5714  0.006598 0.006339 0.9988 0.5707  0.006597 0.006339   5.2      1.9   
## 44 0.2332  0.006382 0.006339 0.9988 0.2329  0.006382 0.006339   1.2      0.4   
## 45 0.4062  0.006470 0.006339 0.9988 0.4057  0.006470 0.006339   1.8      0.5   
## 46 0.3871  0.006458 0.006339 0.9988 0.3866  0.006458 0.006339   2        0.6000
## 47 0.5161  0.006550 0.006339 0.9988 0.5155  0.006550 0.006339   3        0.3000
## 48 0.6096  0.006634 0.006339 0.9988 0.6088  0.006633 0.006339   3        0.5   
## 49 0.3132  0.006417 0.006339 0.9988 0.3128  0.006417 0.006339   0.9      0.1000
## 50 0.4842  0.006525 0.006339 0.9988 0.4836  0.006524 0.006339   2.100    0.2000
## 51 0.4281  0.008044 0.007865 0.9985 0.4275  0.008043 0.007865   5.8      3.2   
## 52 0.4008  0.008022 0.007865 0.9985 0.4002  0.008021 0.007865   5.9      3.2   
## 53 0.4217  0.008039 0.007865 0.9985 0.4210  0.008038 0.007865   4.8      2.3   
## 54 0.03393 0.007866 0.007865 0.9985 0.03388 0.007866 0.007865   1.1      0.9   
## 55 0.09858 0.007874 0.007865 0.9985 0.09843 0.007874 0.007865   1.6      1.2   
## 56 0.1641  0.007891 0.007865 0.9985 0.1639  0.007891 0.007865   1.8      1.1   
## 57 0.2472  0.007924 0.007865 0.9985 0.2469  0.007924 0.007865   2.300    0.8000
## 58 0.3164  0.007963 0.007865 0.9985 0.3160  0.007962 0.007865   2.400    1.100 
## 59 0.2165  0.007911 0.007865 0.9985 0.2162  0.007910 0.007865   0.7000   0.2000
## 60 0.2790  0.007941 0.007865 0.9985 0.2786  0.007941 0.007865   1.8      0.8000
## 61 0.4913  0.009548 0.009269 0.9983 0.4905  0.009547 0.009269   6.3      3.3   
## 62 0.4258  0.009478 0.009269 0.9983 0.4251  0.009477 0.009269   6.4      3.5   
## 63 0.4170  0.009470 0.009269 0.9983 0.4163  0.009469 0.009269   5.1      2.5   
## 64 0.3163  0.009384 0.009269 0.9983 0.3158  0.009384 0.009269   1.7      0.7   
## 65 0.3339  0.009398 0.009269 0.9983 0.3333  0.009397 0.009269   2.1      1     
## 66 0.3454  0.009407 0.009269 0.9983 0.3448  0.009406 0.009269   2.2      1     
## 67 0.3297  0.009394 0.009269 0.9983 0.3291  0.009394 0.009269   2.600    0.7000
## 68 0.2381  0.009334 0.009269 0.9983 0.2377  0.009334 0.009269   2.400    1.400 
## 69 0.08525 0.009277 0.009269 0.9983 0.08510 0.009277 0.009269   0.7000   0.5   
## 70 0.2649  0.009350 0.009269 0.9983 0.2645  0.009350 0.009269   2        1.100 
## 71 0.5237  0.01072  0.01036  0.9981 0.5227  0.01072  0.01036    6.7      3.4   
## 72 0.4100  0.01058  0.01036  0.9981 0.4092  0.01058  0.01036    6.6      3.8   
## 73 0.5129  0.01070  0.01036  0.9981 0.5119  0.01070  0.01036    5.5      2.3   
## 74 0.4343  0.01061  0.01036  0.9981 0.4335  0.01061  0.01036    1.6      0.2000
## 75 0.4383  0.01061  0.01036  0.9981 0.4374  0.01061  0.01036    2.1      0.6000
## 76 0.4602  0.01064  0.01036  0.9981 0.4593  0.01064  0.01036    2.3      0.7   
## 77 0.5568  0.01076  0.01036  0.9981 0.5557  0.01076  0.01036    3.5      0.3000
## 78 0.4214  0.01059  0.01036  0.9981 0.4206  0.01059  0.01036    3.3      1.400 
## 79 0.2889  0.01047  0.01036  0.9981 0.2884  0.01047  0.01036    0.9      0.2000
## 80 0.3293  0.01050  0.01036  0.9981 0.3286  0.01050  0.01036    2.6      1.3   
##       d_DD     vd_DD     Wd_DD    g_DD     vg_DD     Wg_DD avg_cl_size
##      <dbl>     <dbl>     <dbl>   <dbl>     <dbl>     <dbl>       <dbl>
##  1 0.6121  0.001458  0.001282  0.6117  0.001458  0.001282            9
##  2 0.4949  0.0005678 0.0004524 0.4945  0.0005677 0.0004524           9
##  3 0.3962  0.003090  0.003016  0.3959  0.003090  0.003016            9
##  4 0.1639  0.002803  0.002790  0.1638  0.002803  0.002790            9
##  5 0.3278  0.002313  0.002262  0.3276  0.002313  0.002262            9
##  6 0.3331  0.002993  0.002941  0.3328  0.002993  0.002941            9
##  7 0.3956  0.002600  0.002526  0.3953  0.002600  0.002526            9
##  8 0.3797  0.002149  0.002081  0.3795  0.002149  0.002081            9
##  9 0.1250  0.002081  0.002074  0.1249  0.002081  0.002074            9
## 10 0.2018  0.003035  0.003016  0.2017  0.003035  0.003016            9
## 11 0.3493  0.001339  0.001282  0.3491  0.001339  0.001282            9
## 12 0.3223  0.0005014 0.0004524 0.3221  0.0005013 0.0004524           9
## 13 0.3077  0.003061  0.003016  0.3075  0.003061  0.003016            9
## 14 0.1333  0.002798  0.002790  0.1332  0.002798  0.002790            9
## 15 0.1332  0.002270  0.002262  0.1331  0.002270  0.002262            9
## 16 0.1935  0.002958  0.002941  0.1934  0.002958  0.002941            9
## 17 0.2127  0.002547  0.002526  0.2126  0.002547  0.002526            9
## 18 0.1974  0.002100  0.002081  0.1973  0.002099  0.002081            9
## 19 0.08696 0.002077  0.002074  0.08690 0.002077  0.002074            9
## 20 0.07999 0.003019  0.003016  0.07994 0.003019  0.003016            9
## 21 0.3998  0.001357  0.001282  0.3995  0.001357  0.001282            9
## 22 0.3359  0.0005056 0.0004524 0.3356  0.0005055 0.0004524           9
## 23 0.2935  0.003057  0.003016  0.2933  0.003057  0.003016            9
## 24 0.2372  0.002816  0.002790  0.2371  0.002816  0.002790            9
## 25 0.2539  0.002292  0.002262  0.2537  0.002292  0.002262            9
## 26 0.3491  0.002998  0.002941  0.3489  0.002998  0.002941            9
## 27 0.2247  0.002550  0.002526  0.2245  0.002550  0.002526            9
## 28 0.1558  0.002093  0.002081  0.1557  0.002093  0.002081            9
## 29 0.08490 0.002077  0.002074  0.08484 0.002077  0.002074            9
## 30 0.04167 0.003017  0.003016  0.04164 0.003017  0.003016            9
## 31 0.3782  0.001349  0.001282  0.3779  0.001349  0.001282            9
## 32 0.2949  0.0004934 0.0004524 0.2947  0.0004933 0.0004524           9
## 33 0.2857  0.003055  0.003016  0.2855  0.003055  0.003016            9
## 34 0.2461  0.002818  0.002790  0.2459  0.002818  0.002790            9
## 35 0.2941  0.002303  0.002262  0.2939  0.002303  0.002262            9
## 36 0.3691  0.003005  0.002941  0.3689  0.003005  0.002941            9
## 37 0.4807  0.002635  0.002526  0.4804  0.002635  0.002526            9
## 38 0.4666  0.002184  0.002081  0.4663  0.002184  0.002081            9
## 39 0.5816  0.002233  0.002074  0.5812  0.002233  0.002074            9
## 40 0.6406  0.003210  0.003016  0.6402  0.003209  0.003016            9
## 41 0.9316  0.002843  0.002155  0.9305  0.002841  0.002155            9
## 42 0.7909  0.001256  0.0007607 0.7900  0.001255  0.0007607           9
## 43 0.6286  0.005384  0.005071  0.6278  0.005384  0.005071            9
## 44 0.2665  0.004747  0.004691  0.2662  0.004747  0.004691            9
## 45 0.4401  0.003957  0.003803  0.4396  0.003957  0.003803            9
## 46 0.4516  0.005106  0.004945  0.4511  0.005106  0.004945            9
## 47 0.5806  0.004514  0.004247  0.5799  0.004514  0.004247            9
## 48 0.6096  0.003794  0.003499  0.6088  0.003793  0.003499            9
## 49 0.3132  0.003564  0.003487  0.3128  0.003564  0.003487            9
## 50 0.4000  0.005198  0.005071  0.3995  0.005198  0.005071            9
## 51 0.5059  0.002924  0.002674  0.5052  0.002924  0.002674            9
## 52 0.4328  0.001127  0.0009438 0.4322  0.001127  0.0009438           9
## 53 0.4792  0.006516  0.006292  0.4784  0.006516  0.006292            9
## 54 0.06787 0.005824  0.005820  0.06777 0.005824  0.005820            9
## 55 0.1314  0.004736  0.004719  0.1312  0.004736  0.004719            9
## 56 0.2297  0.006186  0.006134  0.2294  0.006186  0.006134            9
## 57 0.3090  0.005363  0.005269  0.3086  0.005363  0.005269            9
## 58 0.3164  0.004439  0.004341  0.3160  0.004439  0.004341            9
## 59 0.2165  0.004371  0.004326  0.2162  0.004371  0.004326            9
## 60 0.1993  0.006331  0.006292  0.1990  0.006330  0.006292            9
## 61 0.5669  0.003522  0.003151  0.5659  0.003521  0.003151            9
## 62 0.4574  0.001354  0.001112  0.4566  0.001353  0.001112            9
## 63 0.4714  0.007672  0.007415  0.4706  0.007671  0.007415            9
## 64 0.3515  0.007002  0.006859  0.3509  0.007001  0.006859            9
## 65 0.3673  0.005717  0.005561  0.3666  0.005716  0.005561            9
## 66 0.4145  0.007428  0.007230  0.4137  0.007427  0.007230            9
## 67 0.3915  0.006387  0.006210  0.3908  0.006386  0.006210            9
## 68 0.2381  0.005182  0.005116  0.2377  0.005182  0.005116            9
## 69 0.08525 0.005106  0.005098  0.08510 0.005106  0.005098            9
## 70 0.1834  0.007454  0.007415  0.1831  0.007454  0.007415            9
## 71 0.5959  0.003981  0.003524  0.5948  0.003979  0.003524            9
## 72 0.4416  0.001495  0.001244  0.4407  0.001494  0.001244            9
## 73 0.5660  0.008703  0.008291  0.5649  0.008702  0.008291            9
## 74 0.4677  0.007951  0.007669  0.4668  0.007950  0.007669            9
## 75 0.4696  0.006502  0.006218  0.4687  0.006501  0.006218            9
## 76 0.5260  0.008440  0.008083  0.5249  0.008438  0.008083            9
## 77 0.6144  0.007430  0.006943  0.6132  0.007428  0.006943            9
## 78 0.4214  0.005949  0.005721  0.4206  0.005949  0.005721            9
## 79 0.2889  0.005807  0.005700  0.2884  0.005807  0.005700            9
## 80 0.2518  0.008372  0.008291  0.2513  0.008372  0.008291            9
##    avg_cl_type            icc icc_type n_covariates gamma_sqrt df_adj  omega
##    <chr>                <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
##  1 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
##  2 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
##  3 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
##  4 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
##  5 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
##  6 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
##  7 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
##  8 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
##  9 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 10 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 11 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 12 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 13 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 14 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 15 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 16 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 17 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 18 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 19 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 20 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 21 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 22 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 23 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 24 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 25 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 26 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 27 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 28 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 29 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 30 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 31 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 32 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 33 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 34 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 35 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 36 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 37 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 38 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 39 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 40 From study (p. 3338)   0.1 Imputed             1      0.974 1017.  0.9993
## 41 From study (p. 3338)   0.1 Imputed             1      0.974  604.4 0.9988
## 42 From study (p. 3338)   0.1 Imputed             1      0.974  604.4 0.9988
## 43 From study (p. 3338)   0.1 Imputed             1      0.974  604.4 0.9988
## 44 From study (p. 3338)   0.1 Imputed             1      0.974  604.4 0.9988
## 45 From study (p. 3338)   0.1 Imputed             1      0.974  604.4 0.9988
## 46 From study (p. 3338)   0.1 Imputed             1      0.974  604.4 0.9988
## 47 From study (p. 3338)   0.1 Imputed             1      0.974  604.4 0.9988
## 48 From study (p. 3338)   0.1 Imputed             1      0.974  604.4 0.9988
## 49 From study (p. 3338)   0.1 Imputed             1      0.974  604.4 0.9988
## 50 From study (p. 3338)   0.1 Imputed             1      0.974  604.4 0.9988
## 51 From study (p. 3338)   0.1 Imputed             1      0.976  488.0 0.9985
## 52 From study (p. 3338)   0.1 Imputed             1      0.976  488.0 0.9985
## 53 From study (p. 3338)   0.1 Imputed             1      0.976  488.0 0.9985
## 54 From study (p. 3338)   0.1 Imputed             1      0.976  488.0 0.9985
## 55 From study (p. 3338)   0.1 Imputed             1      0.976  488.0 0.9985
## 56 From study (p. 3338)   0.1 Imputed             1      0.976  488.0 0.9985
## 57 From study (p. 3338)   0.1 Imputed             1      0.976  488.0 0.9985
## 58 From study (p. 3338)   0.1 Imputed             1      0.976  488.0 0.9985
## 59 From study (p. 3338)   0.1 Imputed             1      0.976  488.0 0.9985
## 60 From study (p. 3338)   0.1 Imputed             1      0.976  488.0 0.9985
## 61 From study (p. 3338)   0.1 Imputed             1      0.975  413.5 0.9982
## 62 From study (p. 3338)   0.1 Imputed             1      0.975  413.5 0.9982
## 63 From study (p. 3338)   0.1 Imputed             1      0.975  413.5 0.9982
## 64 From study (p. 3338)   0.1 Imputed             1      0.975  413.5 0.9982
## 65 From study (p. 3338)   0.1 Imputed             1      0.975  413.5 0.9982
## 66 From study (p. 3338)   0.1 Imputed             1      0.975  413.5 0.9982
## 67 From study (p. 3338)   0.1 Imputed             1      0.975  413.5 0.9982
## 68 From study (p. 3338)   0.1 Imputed             1      0.975  413.5 0.9982
## 69 From study (p. 3338)   0.1 Imputed             1      0.975  413.5 0.9982
## 70 From study (p. 3338)   0.1 Imputed             1      0.975  413.5 0.9982
## 71 From study (p. 3338)   0.1 Imputed             1      0.975  370.2 0.9980
## 72 From study (p. 3338)   0.1 Imputed             1      0.975  370.2 0.9980
## 73 From study (p. 3338)   0.1 Imputed             1      0.975  370.2 0.9980
## 74 From study (p. 3338)   0.1 Imputed             1      0.975  370.2 0.9980
## 75 From study (p. 3338)   0.1 Imputed             1      0.975  370.2 0.9980
## 76 From study (p. 3338)   0.1 Imputed             1      0.975  370.2 0.9980
## 77 From study (p. 3338)   0.1 Imputed             1      0.975  370.2 0.9980
## 78 From study (p. 3338)   0.1 Imputed             1      0.975  370.2 0.9980
## 79 From study (p. 3338)   0.1 Imputed             1      0.975  370.2 0.9980
## 80 From study (p. 3338)   0.1 Imputed             1      0.975  370.2 0.9980
##    gt_post vgt_post Wgt_post   gt_DD    vgt_DD    Wgt_DD   hg_DD    vhg_DD
##      <dbl>    <dbl>    <dbl>   <dbl>     <dbl>     <dbl>   <dbl>     <dbl>
##  1 0.5189  0.005234 0.005101 0.5958  0.001910  0.001734  0.4424  0.0009879
##  2 0.4495  0.005201 0.005101 0.4816  0.0007267 0.0006121 0.5942  0.0009879
##  3 0.3305  0.005155 0.005101 0.3856  0.004154  0.004081  0.1892  0.0009879
##  4 0.1276  0.005109 0.005101 0.1595  0.003787  0.003775  0.08156 0.0009879
##  5 0.2871  0.005142 0.005101 0.3190  0.003111  0.003061  0.1808  0.0009879
##  6 0.2593  0.005134 0.005101 0.3242  0.004031  0.003979  0.1612  0.0009879
##  7 0.3209  0.005152 0.005101 0.3850  0.003491  0.003418  0.2063  0.0009879
##  8 0.3696  0.005169 0.005101 0.3696  0.002883  0.002816  0.2181  0.0009879
##  9 0.1216  0.005108 0.005101 0.1216  0.002813  0.002806  0.07214 0.0009879
## 10 0.2678  0.005137 0.005101 0.1964  0.004100  0.004081  0.09656 0.0009879
## 11 0.2644  0.005136 0.005101 0.3400  0.001791  0.001734  0.2552  0.0009879
## 12 0.2824  0.005140 0.005101 0.3137  0.0006607 0.0006121 0.3935  0.0009879
## 13 0.2433  0.005130 0.005101 0.2995  0.004125  0.004081  0.1471  0.0009879
## 14 0.09733 0.005106 0.005101 0.1298  0.003783  0.003775  0.06636 0.0009879
## 15 0.09725 0.005106 0.005101 0.1297  0.003069  0.003061  0.07364 0.0009879
## 16 0.1256  0.005109 0.005101 0.1884  0.003996  0.003979  0.09380 0.0009879
## 17 0.1449  0.005111 0.005101 0.2071  0.003439  0.003418  0.1112  0.0009879
## 18 0.1922  0.005119 0.005101 0.1922  0.002834  0.002816  0.1137  0.0009879
## 19 0.08463 0.005105 0.005101 0.08463 0.002809  0.002806  0.05021 0.0009879
## 20 0.1557  0.005113 0.005101 0.07786 0.004084  0.004081  0.03830 0.0009879
## 21 0.3150  0.005150 0.005101 0.3891  0.001809  0.001734  0.2916  0.0009879
## 22 0.2957  0.005144 0.005101 0.3269  0.0006649 0.0006121 0.4095  0.0009879
## 23 0.2321  0.005128 0.005101 0.2857  0.004121  0.004081  0.1403  0.0009879
## 24 0.1979  0.005120 0.005101 0.2309  0.003801  0.003775  0.1180  0.0009879
## 25 0.2162  0.005124 0.005101 0.2471  0.003091  0.003061  0.1402  0.0009879
## 26 0.2780  0.005139 0.005101 0.3398  0.004036  0.003979  0.1689  0.0009879
## 27 0.1531  0.005113 0.005101 0.2187  0.003441  0.003418  0.1174  0.0009879
## 28 0.1517  0.005112 0.005101 0.1517  0.002827  0.002816  0.08979 0.0009879
## 29 0.08263 0.005104 0.005101 0.08263 0.002809  0.002806  0.04902 0.0009879
## 30 0.1217  0.005108 0.005101 0.04055 0.004082  0.004081  0.01995 0.0009879
## 31 0.2980  0.005145 0.005101 0.3681  0.001801  0.001734  0.2760  0.0009879
## 32 0.2551  0.005133 0.005101 0.2870  0.0006528 0.0006121 0.3607  0.0009879
## 33 0.2259  0.005126 0.005101 0.2781  0.004119  0.004081  0.1366  0.0009879
## 34 0.2096  0.005123 0.005101 0.2395  0.003803  0.003775  0.1224  0.0009879
## 35 0.2576  0.005134 0.005101 0.2863  0.003101  0.003061  0.1623  0.0009879
## 36 0.2994  0.005145 0.005101 0.3593  0.004043  0.003979  0.1786  0.0009879
## 37 0.4117  0.005185 0.005101 0.4679  0.003526  0.003418  0.2503  0.0009879
## 38 0.4542  0.005203 0.005101 0.4542  0.002918  0.002816  0.2674  0.0009879
## 39 0.5661  0.005259 0.005101 0.5661  0.002964  0.002806  0.3328  0.0009879
## 40 0.6991  0.005342 0.005101 0.6235  0.004273  0.004081  0.3044  0.0009879
## 41 0.8275  0.009134 0.008564 0.9063  0.003595  0.002912  0.6606  0.001663 
## 42 0.7367  0.009015 0.008564 0.7694  0.001520  0.001028  0.9138  0.001663 
## 43 0.5559  0.008821 0.008564 0.6115  0.007162  0.006851  0.2990  0.001663 
## 44 0.2268  0.008607 0.008564 0.2593  0.006393  0.006338  0.1326  0.001663 
## 45 0.3952  0.008694 0.008564 0.4281  0.005291  0.005139  0.2424  0.001663 
## 46 0.3766  0.008682 0.008564 0.4393  0.006841  0.006680  0.2183  0.001663 
## 47 0.5021  0.008774 0.008564 0.5648  0.006003  0.005738  0.3018  0.001663 
## 48 0.5930  0.008857 0.008564 0.5930  0.005020  0.004727  0.3482  0.001663 
## 49 0.3047  0.008641 0.008564 0.3047  0.004788  0.004710  0.1806  0.001663 
## 50 0.4710  0.008749 0.008564 0.3891  0.006977  0.006851  0.1911  0.001663 
## 51 0.4172  0.01055  0.01037  0.4930  0.003777  0.003527  0.3725  0.002060 
## 52 0.3906  0.01053  0.01037  0.4218  0.001428  0.001245  0.5301  0.002060 
## 53 0.4109  0.01055  0.01037  0.4669  0.008523  0.008299  0.2316  0.002060 
## 54 0.03307 0.01037  0.01037  0.06614 0.007681  0.007676  0.03426 0.002060 
## 55 0.09606 0.01038  0.01037  0.1281  0.006241  0.006224  0.07365 0.002060 
## 56 0.1599  0.01040  0.01037  0.2239  0.008143  0.008091  0.1128  0.002060 
## 57 0.2409  0.01043  0.01037  0.3012  0.007044  0.006950  0.1636  0.002060 
## 58 0.3084  0.01047  0.01037  0.3084  0.005824  0.005726  0.1844  0.002060 
## 59 0.2110  0.01042  0.01037  0.2110  0.005751  0.005705  0.1266  0.002060 
## 60 0.2719  0.01045  0.01037  0.1942  0.008338  0.008299  0.09669 0.002060 
## 61 0.4782  0.01255  0.01227  0.5517  0.004542  0.004172  0.4152  0.002431 
## 62 0.4144  0.01248  0.01227  0.4451  0.001714  0.001473  0.5574  0.002431 
## 63 0.4059  0.01247  0.01227  0.4588  0.01007   0.009817  0.2273  0.002431 
## 64 0.3078  0.01239  0.01227  0.3421  0.009223  0.009081  0.1765  0.002431 
## 65 0.3249  0.01240  0.01227  0.3574  0.007518  0.007363  0.2047  0.002431 
## 66 0.3361  0.01241  0.01227  0.4034  0.009770  0.009572  0.2026  0.002431 
## 67 0.3209  0.01240  0.01227  0.3810  0.008399  0.008222  0.2065  0.002431 
## 68 0.2317  0.01234  0.01227  0.2317  0.006839  0.006774  0.1386  0.002431 
## 69 0.08297 0.01228  0.01227  0.08297 0.006758  0.006750  0.04979 0.002431 
## 70 0.2578  0.01235  0.01227  0.1785  0.009856  0.009817  0.08878 0.002431 
## 71 0.5096  0.01401  0.01366  0.5799  0.005101  0.004644  0.4365  0.002716 
## 72 0.3990  0.01387  0.01366  0.4297  0.001890  0.001639  0.5399  0.002716 
## 73 0.4991  0.01400  0.01366  0.5507  0.01134   0.01093   0.2729  0.002716 
## 74 0.4226  0.01390  0.01366  0.4551  0.01039   0.01011   0.2348  0.002716 
## 75 0.4265  0.01391  0.01366  0.4569  0.008479  0.008195  0.2615  0.002716 
## 76 0.4478  0.01393  0.01366  0.5118  0.01101   0.01065   0.2570  0.002716 
## 77 0.5418  0.01406  0.01366  0.5978  0.009637  0.009151  0.3229  0.002716 
## 78 0.4101  0.01389  0.01366  0.4101  0.007768  0.007540  0.2449  0.002716 
## 79 0.2811  0.01377  0.01366  0.2811  0.007620  0.007512  0.1686  0.002716 
## 80 0.3204  0.01380  0.01366  0.2450  0.01101   0.01093   0.1220  0.002716 
##      h_DD  df_DD n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop
##     <dbl>  <dbl>           <dbl> <chr>             <dbl>     <dbl>      <dbl>
##  1 1012.  1012.                1 eta               1.353   NA       NA       
##  2 1012.  1012.                1 eta               1.353    0.4821   0.002147
##  3 1012.  1012.                1 eta               1.353   NA       NA       
##  4 1012.  1012.                1 eta               1.353   NA       NA       
##  5 1012.  1012.                1 eta               1.353   NA       NA       
##  6 1012.  1012.                1 eta               1.353   NA       NA       
##  7 1012.  1012.                1 eta               1.353   NA       NA       
##  8 1012.  1012.                1 eta               1.353   NA       NA       
##  9 1012.  1012.                1 eta               1.353   NA       NA       
## 10 1012.  1012.                1 eta               1.353   NA       NA       
## 11 1012.  1012.                1 eta               1.353   NA       NA       
## 12 1012.  1012.                1 eta               1.353    0.3214   0.001289
## 13 1012.  1012.                1 eta               1.353   NA       NA       
## 14 1012.  1012.                1 eta               1.353   NA       NA       
## 15 1012.  1012.                1 eta               1.353   NA       NA       
## 16 1012.  1012.                1 eta               1.353   NA       NA       
## 17 1012.  1012.                1 eta               1.353   NA       NA       
## 18 1012.  1012.                1 eta               1.353   NA       NA       
## 19 1012.  1012.                1 eta               1.353   NA       NA       
## 20 1012.  1012.                1 eta               1.353   NA       NA       
## 21 1012.  1012.                1 eta               1.353   NA       NA       
## 22 1012.  1012.                1 eta               1.353    0.3375   0.001359
## 23 1012.  1012.                1 eta               1.353   NA       NA       
## 24 1012.  1012.                1 eta               1.353   NA       NA       
## 25 1012.  1012.                1 eta               1.353   NA       NA       
## 26 1012.  1012.                1 eta               1.353   NA       NA       
## 27 1012.  1012.                1 eta               1.353   NA       NA       
## 28 1012.  1012.                1 eta               1.353   NA       NA       
## 29 1012.  1012.                1 eta               1.353   NA       NA       
## 30 1012.  1012.                1 eta               1.353   NA       NA       
## 31 1012.  1012.                1 eta               1.353   NA       NA       
## 32 1012.  1012.                1 eta               1.353    0.2893   0.001159
## 33 1012.  1012.                1 eta               1.353   NA       NA       
## 34 1012.  1012.                1 eta               1.353   NA       NA       
## 35 1012.  1012.                1 eta               1.353   NA       NA       
## 36 1012.  1012.                1 eta               1.353   NA       NA       
## 37 1012.  1012.                1 eta               1.353   NA       NA       
## 38 1012.  1012.                1 eta               1.353   NA       NA       
## 39 1012.  1012.                1 eta               1.353   NA       NA       
## 40 1012.  1012.                1 eta               1.353   NA       NA       
## 41  601.3  601.3               1 eta               1.351   NA       NA       
## 42  601.3  601.3               1 eta               1.351    0.7549   0.004797
## 43  601.3  601.3               1 eta               1.351   NA       NA       
## 44  601.3  601.3               1 eta               1.351   NA       NA       
## 45  601.3  601.3               1 eta               1.351   NA       NA       
## 46  601.3  601.3               1 eta               1.351   NA       NA       
## 47  601.3  601.3               1 eta               1.351   NA       NA       
## 48  601.3  601.3               1 eta               1.351   NA       NA       
## 49  601.3  601.3               1 eta               1.351   NA       NA       
## 50  601.3  601.3               1 eta               1.351   NA       NA       
## 51  485.4  485.4               1 eta               1.319   NA       NA       
## 52  485.4  485.4               1 eta               1.319    0.4344   0.002480
## 53  485.4  485.4               1 eta               1.319   NA       NA       
## 54  485.4  485.4               1 eta               1.319   NA       NA       
## 55  485.4  485.4               1 eta               1.319   NA       NA       
## 56  485.4  485.4               1 eta               1.319   NA       NA       
## 57  485.4  485.4               1 eta               1.319   NA       NA       
## 58  485.4  485.4               1 eta               1.319   NA       NA       
## 59  485.4  485.4               1 eta               1.319   NA       NA       
## 60  485.4  485.4               1 eta               1.319   NA       NA       
## 61  411.3  411.3               1 eta               1.324   NA       NA       
## 62  411.3  411.3               1 eta               1.324    0.4660   0.002893
## 63  411.3  411.3               1 eta               1.324   NA       NA       
## 64  411.3  411.3               1 eta               1.324   NA       NA       
## 65  411.3  411.3               1 eta               1.324   NA       NA       
## 66  411.3  411.3               1 eta               1.324   NA       NA       
## 67  411.3  411.3               1 eta               1.324   NA       NA       
## 68  411.3  411.3               1 eta               1.324   NA       NA       
## 69  411.3  411.3               1 eta               1.324   NA       NA       
## 70  411.3  411.3               1 eta               1.324   NA       NA       
## 71  368.2  368.2               1 eta               1.318   NA       NA       
## 72  368.2  368.2               1 eta               1.318    0.4498   0.002959
## 73  368.2  368.2               1 eta               1.318   NA       NA       
## 74  368.2  368.2               1 eta               1.318   NA       NA       
## 75  368.2  368.2               1 eta               1.318   NA       NA       
## 76  368.2  368.2               1 eta               1.318   NA       NA       
## 77  368.2  368.2               1 eta               1.318   NA       NA       
## 78  368.2  368.2               1 eta               1.318   NA       NA       
## 79  368.2  368.2               1 eta               1.318   NA       NA       
## 80  368.2  368.2               1 eta               1.318   NA       NA       
##    Wgt_DD_pop vary_id                                
##         <dbl> <chr>                                  
##  1 NA         ITT/GAD-7/Post                         
##  2  0.0006032 ITT/PHQ-9/Post                         
##  3 NA         ITT/PHQ15/Post                         
##  4 NA         ITT/working_life_Function/Post         
##  5 NA         ITT/Socal_life_Function/Post           
##  6 NA         ITT/Family_life_Function/Post          
##  7 NA         ITT/Physical_QoL/Post                  
##  8 NA         ITT/Psychological_QoL/Post             
##  9 NA         ITT/Social_QoL/Post                    
## 10 NA         ITT/Environment_QoL/Post               
## 11 NA         ITT/GAD-7/3m                           
## 12  0.0006032 ITT/PHQ-9/3m                           
## 13 NA         ITT/PHQ15/3m                           
## 14 NA         ITT/working_life_Function/3m           
## 15 NA         ITT/Socal_life_Function/3m             
## 16 NA         ITT/Family_life_Function/3m            
## 17 NA         ITT/Physical_QoL/3m                    
## 18 NA         ITT/Psychological_QoL/3m               
## 19 NA         ITT/Social_QoL/3m                      
## 20 NA         ITT/Environment_QoL/3m                 
## 21 NA         ITT/GAD-7/6m                           
## 22  0.0006032 ITT/PHQ-9/6m                           
## 23 NA         ITT/PHQ15/6m                           
## 24 NA         ITT/working_life_Function/6m           
## 25 NA         ITT/Socal_life_Function/6m             
## 26 NA         ITT/Family_life_Function/6m            
## 27 NA         ITT/Physical_QoL/6m                    
## 28 NA         ITT/Psychological_QoL/6m               
## 29 NA         ITT/Social_QoL/6m                      
## 30 NA         ITT/Environment_QoL/6m                 
## 31 NA         ITT/GAD-7/12m                          
## 32  0.0006032 ITT/PHQ-9/12m                          
## 33 NA         ITT/PHQ15/12m                          
## 34 NA         ITT/working_life_Function/12m          
## 35 NA         ITT/Socal_life_Function/12m            
## 36 NA         ITT/Family_life_Function/12m           
## 37 NA         ITT/Physical_QoL/12m                   
## 38 NA         ITT/Psychological_QoL/12m              
## 39 NA         ITT/Social_QoL/12m                     
## 40 NA         ITT/Environment_QoL/12m                
## 41 NA         Per-protocol/GAD-7/Post                
## 42  0.001013  Per-protocol/PHQ-9/Post                
## 43 NA         Per-protocol/PHQ15/Post                
## 44 NA         Per-protocol/working_life_Function/Post
## 45 NA         Per-protocol/Socal_life_Function/Post  
## 46 NA         Per-protocol/Family_life_Function/Post 
## 47 NA         Per-protocol/Physical_QoL/Post         
## 48 NA         Per-protocol/Psychological_QoL/Post    
## 49 NA         Per-protocol/Social_QoL/Post           
## 50 NA         Per-protocol/Environment_QoL/Post      
## 51 NA         Per-protocol/GAD-7/3m                  
## 52  0.001227  Per-protocol/PHQ-9/3m                  
## 53 NA         Per-protocol/PHQ15/3m                  
## 54 NA         Per-protocol/working_life_Function/3m  
## 55 NA         Per-protocol/Socal_life_Function/3m    
## 56 NA         Per-protocol/Family_life_Function/3m   
## 57 NA         Per-protocol/Physical_QoL/3m           
## 58 NA         Per-protocol/Psychological_QoL/3m      
## 59 NA         Per-protocol/Social_QoL/3m             
## 60 NA         Per-protocol/Environment_QoL/3m        
## 61 NA         Per-protocol/GAD-7/6m                  
## 62  0.001451  Per-protocol/PHQ-9/6m                  
## 63 NA         Per-protocol/PHQ15/6m                  
## 64 NA         Per-protocol/working_life_Function/6m  
## 65 NA         Per-protocol/Socal_life_Function/6m    
## 66 NA         Per-protocol/Family_life_Function/6m   
## 67 NA         Per-protocol/Physical_QoL/6m           
## 68 NA         Per-protocol/Psychological_QoL/6m      
## 69 NA         Per-protocol/Social_QoL/6m             
## 70 NA         Per-protocol/Environment_QoL/6m        
## 71 NA         Per-protocol/GAD-7/12m                 
## 72  0.001615  Per-protocol/PHQ-9/12m                 
## 73 NA         Per-protocol/PHQ15/12m                 
## 74 NA         Per-protocol/working_life_Function/12m 
## 75 NA         Per-protocol/Socal_life_Function/12m   
## 76 NA         Per-protocol/Family_life_Function/12m  
## 77 NA         Per-protocol/Physical_QoL/12m          
## 78 NA         Per-protocol/Psychological_QoL/12m     
## 79 NA         Per-protocol/Social_QoL/12m            
## 80 NA         Per-protocol/Environment_QoL/12m

Craigie & Nathan (2009) (Quality-checked)

Entering data from Table 2 and Treatment Format \(\times\) Time interaction estimate from text results reported on pages 307-08

Craigie2009 <- tibble(
  analysis = rep(c(
    "ITT",
    "Completers"), each = 6),
  
  outcome = rep(c(
  "BDI_II", 
  "BAI",
  "Q_LES_Q"), each = 2,2),
  
  group = rep(c(
    "Individual",
    "Group"), each = 1, 6), 
  
  N = c(rep(c(116, 240), 3),
        rep(c(77, 157), 3)
        
  ),
  
  N_start = rep(c(116, 240), 6),
  
  # pre-treatment means 
  m_pre = c( 
    # Intention to treat
    30.0, 31.3, # BDI-II
    21.1, 20.2, # BAI 
    43.2, 42.3, # Q-LES-Q
    # Completers (treatment-on-treated)
    30.8, 30.7,
    20.7, 19.6,
    46.4, 42.9
  ), 
  
  # pre-treatment standard deviation 
  sd_pre = c(
    # Intention to treat
    10.4, 11.2,
    12.2, 11.3,
    14.7, 14.1,
    # Completers (treatment-on-treated)
    10.3, 11.1,
    12.0, 11.0,
    13.5, 13.8
  ),
  
  # post-treatment means
  m_post = c(
    # Intention to treat
    16.3, 20.9,
    12.9, 15.6,
    52.3, 50.8,
    # Completers (treatment-on-treated)
    11.7, 17.7,
    9.2, 13.7,
    62.7, 55.3
  ),
  
  # post-treatment means 
  sd_post = c(
    # Intention to treat
    12.2, 13.6,
    11.2, 11.4,
    19.9, 17.9,
    # Completers (treatment-on-treated)
    9.9, 12.4,
    8.3, 10.7,
    14.7, 15.8
  ),
  
  F_within = c(
    129.79, 187.21,
    64.96, 56.00,
    42.27, 79.70,
    
    192.23, 200.75, 
    86.27, 61.57,
    62.45, 101.19
  ),
  
  # From Wilson (2016) Eq 31.
  r = ((sd_pre^2*F_within + sd_post^2*F_within) - 
        (m_post-m_pre)^2 * N)/ (2 * sd_pre*sd_post*F_within),
    
  # For pooling of correlation coefficients 
  z = 0.5 * log( (1+r)/(1-r) ),
  v = 1/(N-3),
  w = 1/v
  
) 

Effect size calculation

# Calculating preposttest-correlations across groups
ppcors_Craigie2009 <- 
  Craigie2009 |> 
  summarise(
    mean_z = sum(w*z)/sum(w),
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "F_values used, but calcuable from study and used in the population-based effect sizes",
    .by = c(analysis, outcome)
  )

ppcors_Craigie2009  
## # A tibble: 6 × 5
##   analysis   outcome mean_z  ppcor
##   <chr>      <chr>    <dbl>  <dbl>
## 1 ITT        BDI_II  0.5508 0.5012
## 2 ITT        BAI     0.7290 0.6225
## 3 ITT        Q_LES_Q 0.7215 0.6179
## 4 Completers BDI_II  0.4897 0.4540
## 5 Completers BAI     0.6615 0.5793
## 6 Completers Q_LES_Q 0.3962 0.3767
##   ppcor_method                                                                  
##   <chr>                                                                         
## 1 F_values used, but calcuable from study and used in the population-based effe…
## 2 F_values used, but calcuable from study and used in the population-based effe…
## 3 F_values used, but calcuable from study and used in the population-based effe…
## 4 F_values used, but calcuable from study and used in the population-based effe…
## 5 F_values used, but calcuable from study and used in the population-based effe…
## 6 F_values used, but calcuable from study and used in the population-based effe…
Craigie2009_est <- 
  Craigie2009 |> 
  mutate(group = case_match(group, "Group" ~ "t", "Individual" ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    ) |> 
  left_join(ppcors_Craigie2009, by = join_by(analysis, outcome)) |> 
  mutate(
  
  analysis_plan = rep(c("All mental health outcomes/Depression", "All mental health outcomes/Anxiety", 
                        "Wellbeing and Quality of Life"), 2),  
  effect_size = "SMD",
  sd_used = "Pooled posttest SD",
    
  study = "Craigie & Nathan 2009",
  main_es_method = "Raw diff-in-diffs",
  
  #ppcor_calc_method = "Not relevant - F_values used",
  
  F_val = c(
    5.96, 9.84, 0.17, # (entered from p. 307 left side bottom)
    13.86, 16.93, 2.74 # (entered from p. 308 right side top)
  ),
    
  N_total = N_t + N_c,
  df_ind = N_total,
  
  # Calculate d post
  m_post = if_else(outcome == "Q_LES_Q", m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
  sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
  
  d_post = m_post/sd_pool, 
  vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
  Wd_post = (1/N_t + 1/N_c), 
  
  J = 1 - 3/(4*df_ind-1),
    
  g_post = J * d_post,
  vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
  Wg_post = Wd_post,
  
  # Account for the fact that some outcomes are on different scales
  m_diff_t = if_else(outcome == "Q_LES_Q", m_post_t - m_pre_t, (m_post_t - m_pre_t) * -1),
  m_diff_c = if_else(outcome == "Q_LES_Q", m_post_c - m_pre_c, (m_post_c - m_pre_c) * -1),
    
  d_DD = (m_diff_t - m_diff_c)/sd_pool,
  vd_DD = d_DD^2/F_val + d_DD^2/(2*df_ind),
  Wd_DD = d_DD^2/F_val,
  
  g_DD = J * d_DD, 
  vg_DD = g_DD^2/F_val + g_DD^2/(2*df_ind),
  Wg_DD = g_DD^2/F_val
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # "groups consisted of between 5 to 12 patients" p. 306
    avg_cl_size = 8.5, 
    avg_cl_type = "From study (p. 305)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = vgt_post:Wgt_post
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      F_val = F_val,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste0(analysis, "/", outcome)
    
  ) |> 
  ungroup(); Craigie2009_est
## # A tibble: 6 × 77
##   analysis   outcome   N_c   N_t N_start_c N_start_t m_pre_c m_pre_t sd_pre_c
##   <chr>      <chr>   <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>
## 1 ITT        BDI_II    116   240       116       240    30      31.3     10.4
## 2 ITT        BAI       116   240       116       240    21.1    20.2     12.2
## 3 ITT        Q_LES_Q   116   240       116       240    43.2    42.3     14.7
## 4 Completers BDI_II     77   157       116       240    30.8    30.7     10.3
## 5 Completers BAI        77   157       116       240    20.7    19.6     12  
## 6 Completers Q_LES_Q    77   157       116       240    46.4    42.9     13.5
##   sd_pre_t m_post_c m_post_t sd_post_c sd_post_t F_within_c F_within_t    r_c
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl>      <dbl>      <dbl>  <dbl>
## 1     11.2     16.3     20.9      12.2      13.6     129.8      187.2  0.3517
## 2     11.3     12.9     15.6      11.2      11.4      64.96      56    0.5643
## 3     14.1     52.3     50.8      19.9      17.9      42.27      79.7  0.6578
## 4     11.1     11.7     17.7       9.9      12.4     192.2      200.8  0.2843
## 5     11        9.2     13.7       8.3      10.7      86.27      61.57 0.4762
## 6     13.8     62.7     55.3      14.7      15.8      62.45     101.2  0.1783
##      r_t    z_c    z_t      v_c      v_t   w_c   w_t mean_z  ppcor
##    <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <dbl> <dbl>  <dbl>  <dbl>
## 1 0.5637 0.3674 0.6383 0.008850 0.004219   113   237 0.5508 0.5012
## 2 0.6481 0.6391 0.7719 0.008850 0.004219   113   237 0.7290 0.6225
## 3 0.5976 0.7889 0.6894 0.008850 0.004219   113   237 0.7215 0.6179
## 4 0.5260 0.2923 0.5846 0.01351  0.006494    74   154 0.4897 0.4540
## 5 0.6233 0.5180 0.7304 0.01351  0.006494    74   154 0.6615 0.5793
## 6 0.4621 0.1802 0.5000 0.01351  0.006494    74   154 0.3962 0.3767
##   ppcor_method                                                                  
##   <chr>                                                                         
## 1 F_values used, but calcuable from study and used in the population-based effe…
## 2 F_values used, but calcuable from study and used in the population-based effe…
## 3 F_values used, but calcuable from study and used in the population-based effe…
## 4 F_values used, but calcuable from study and used in the population-based effe…
## 5 F_values used, but calcuable from study and used in the population-based effe…
## 6 F_values used, but calcuable from study and used in the population-based effe…
##   analysis_plan                         effect_size sd_used           
##   <chr>                                 <chr>       <chr>             
## 1 All mental health outcomes/Depression SMD         Pooled posttest SD
## 2 All mental health outcomes/Anxiety    SMD         Pooled posttest SD
## 3 Wellbeing and Quality of Life         SMD         Pooled posttest SD
## 4 All mental health outcomes/Depression SMD         Pooled posttest SD
## 5 All mental health outcomes/Anxiety    SMD         Pooled posttest SD
## 6 Wellbeing and Quality of Life         SMD         Pooled posttest SD
##   study                 main_es_method    F_val N_total df_ind m_post sd_pool
##   <chr>                 <chr>             <dbl>   <dbl>  <dbl>  <dbl>   <dbl>
## 1 Craigie & Nathan 2009 Raw diff-in-diffs  5.96     356    356 -4.6    13.16 
## 2 Craigie & Nathan 2009 Raw diff-in-diffs  9.84     356    356 -2.7    11.34 
## 3 Craigie & Nathan 2009 Raw diff-in-diffs  0.17     356    356 -1.5    18.57 
## 4 Craigie & Nathan 2009 Raw diff-in-diffs 13.86     234    234 -6      11.64 
## 5 Craigie & Nathan 2009 Raw diff-in-diffs 16.93     234    234 -4.5     9.978
## 6 Craigie & Nathan 2009 Raw diff-in-diffs  2.74     234    234 -7.400  15.45 
##     d_post vd_post Wd_post      J   g_post vg_post Wg_post m_diff_t m_diff_c
##      <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 -0.3495  0.01296 0.01279 0.9979 -0.3488  0.01296 0.01279     10.4   13.7  
## 2 -0.2382  0.01287 0.01279 0.9979 -0.2377  0.01287 0.01279      4.6    8.2  
## 3 -0.08076 0.01280 0.01279 0.9979 -0.08059 0.01280 0.01279      8.5    9.100
## 4 -0.5154  0.01992 0.01936 0.9968 -0.5138  0.01992 0.01936     13     19.1  
## 5 -0.4510  0.01979 0.01936 0.9968 -0.4496  0.01979 0.01936      5.9   11.5  
## 6 -0.4790  0.01985 0.01936 0.9968 -0.4775  0.01984 0.01936     12.4   16.3  
##       d_DD    vd_DD    Wd_DD     g_DD    vg_DD    Wg_DD avg_cl_size
##      <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>       <dbl>
## 1 -0.2507  0.01064  0.01055  -0.2502  0.01059  0.01050          8.5
## 2 -0.3176  0.01039  0.01025  -0.3169  0.01035  0.01021          8.5
## 3 -0.03230 0.006140 0.006139 -0.03224 0.006114 0.006113         8.5
## 4 -0.5240  0.02040  0.01981  -0.5224  0.02027  0.01969          8.5
## 5 -0.5613  0.01928  0.01861  -0.5595  0.01916  0.01849          8.5
## 6 -0.2525  0.02340  0.02326  -0.2516  0.02325  0.02311          8.5
##   avg_cl_type           icc icc_type n_covariates gamma_sqrt df_adj  omega
##   <chr>               <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
## 1 From study (p. 305)   0.1 Imputed             1      0.983  336.6 0.9978
## 2 From study (p. 305)   0.1 Imputed             1      0.983  336.6 0.9978
## 3 From study (p. 305)   0.1 Imputed             1      0.983  336.6 0.9978
## 4 From study (p. 305)   0.1 Imputed             1      0.982  220.8 0.9966
## 5 From study (p. 305)   0.1 Imputed             1      0.982  220.8 0.9966
## 6 From study (p. 305)   0.1 Imputed             1      0.982  220.8 0.9966
##    gt_post vgt_post Wgt_post    gt_DD   vgt_DD   Wgt_DD    hg_DD   vhg_DD  h_DD
##      <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl> <dbl>
## 1 -0.3428   0.01523  0.01505 -0.2459  0.01203  0.01194  -0.1227  0.002980 335.5
## 2 -0.2336   0.01513  0.01505 -0.3115  0.01175  0.01161  -0.1575  0.002980 335.5
## 3 -0.07921  0.01506  0.01505 -0.03168 0.006952 0.006951 -0.02075 0.002980 335.5
## 4 -0.5045   0.02342  0.02284 -0.5129  0.02299  0.02239  -0.2300  0.004544 220.1
## 5 -0.4414   0.02328  0.02284 -0.5493  0.02171  0.02103  -0.2540  0.004544 220.1
## 6 -0.4688   0.02334  0.02284 -0.2471  0.02643  0.02629  -0.1026  0.004544 220.1
##   df_DD n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop Wgt_DD_pop
##   <dbl>           <dbl> <chr>             <dbl>     <dbl>      <dbl>      <dbl>
## 1 335.5               1 eta               1.177   -0.2702    0.01471    0.01459
## 2 335.5               1 eta               1.177   NA        NA         NA      
## 3 335.5               1 eta               1.177   NA        NA         NA      
## 4 220.1               1 eta               1.18    -0.4984    0.02465    0.02423
## 5 220.1               1 eta               1.18    NA        NA         NA      
## 6 220.1               1 eta               1.18    NA        NA         NA      
##   vary_id           
##   <chr>             
## 1 ITT/BDI_II        
## 2 ITT/BAI           
## 3 ITT/Q_LES_Q       
## 4 Completers/BDI_II 
## 5 Completers/BAI    
## 6 Completers/Q_LES_Q

Crawford et al. (2012) (Quality-checked)

Entering data from Table 2 (p. 7)

Crawford2012 <- 
  tibble(
    
    outcome = rep(
      c(
        "Global assessment of functioning (GAF)", "Positive and negative syndrome scale",
        # "Positive symptoms score", "Negative symptoms score", "General symptoms score", 
        "Social functioning", "Wellbeing" 
        # "Satisfaction with care", "Morisky score"
      ), 
      each = 6),
    group = rep(rep(c("Standard care", "Activity groups", "Art therapy"), 2), n_distinct(outcome)),
    timing = rep(rep(c(12, 24), each = n_distinct(group)), n_distinct(outcome)),
    
    N_start = rep(rep(c(137, 140, 140), 2), n_distinct(outcome)),
    N = rep(c(121, 121, 119, 117, 121, 117), n_distinct(outcome)),
    
    m_pre = c(
      rep(c(44.9, 45.0, 44.8), 2),
      rep(c(72.6, 75.3, 74.3), 2),
      #rep(c(17.3, 18.2, 18), 2),
      #rep(c(18.5, 18.7, 18.7), 2),
      #rep(c(36.8, 37.6, 37.6), 2),
      rep(c(8.1,  9., 8.6), 2),
      rep(c(64.5, 59.1, 58.3), 2)
      #rep(c(24.9, 23.8, 24.8), 2),
      #rep(c(1.2, 1.2, 1.0), 2)
    ), 
    
    sd_pre = c(
      rep(c(12.6, 12.7, 13.1), 2),
      rep(c(21.5, 22., 23.7), 2),
      #rep(c(5.6, 6.8, 6.9), 2),
      #rep(c(7.5, 7., 7.1), 2),
      #rep(c(11.3, 12.5, 12.5), 2),
      rep(c(4.7, 4.8, 4.2), 2),
      rep(c(20.6, 19.5, 21.1), 2)
      #rep(c(5.7, 6.2, 5.7), 2),
      #rep(c(1.3, 1.3, 1.2), 2)
    ),
    
    m_post = c(
      45.7, 45.5, 44.9, # GLobal assessment of functioning 12 month
      46.8, 46.4, 45.6, # GLobal assessment of functioning 24 month
      
      71.2, 69.6, 72.7, # Positive and negative syndrome scale 12 m
      68.1, 66.9, 69.2, # Positive and negative syndrome scale 24 m
      
      #16.7, 16.1, 17.3, # Positive symptoms score 12m
      #16.1, 15.6, 16.8, # Positive symptoms score 24m
      #
      #18.2, 17.3, 18.4, # Negative symptoms score 12m
      #17.2, 16.4, 16.9, # Negative symptoms score 24m
      #
      #36.3, 35.9, 37,   # General symptoms score 12m
      #34.9, 34.9, 35.3, # General symptoms score 24m
      
      8.5, 8.1, 8.3, # Social functioning 12m
      8.1, 8, 8.2,   # Social functioning 24m
      
      64.1, 63.6, 59.6, # Wellbeing 12m 
      68.1, 66.1, 65.1 # Wellbeing 24m 
      
      #24.3, 25., 23.6,  # Satisfaction with care 12m
      #24.2, 24.9, 23.1, # Satisfaction with care 24m
      
      #0.7, 0.6, 0.7, # Morisky score 12m
      #0.6, 0.5, 0.6  # Morisky score 24m
      
    ),
    
    sd_post = c(
      14.4, 14.1, 14.6, 
      12.8, 13.6, 13.1, 
      
      24.6, 23.2, 27.3,
      20.7, 23.3, 21.8,
      
      #6.3, 5.9, 7.6,
      #5.5, 6.4, 6.5,
      #
      #7.7, 7.2, 8,
      #7.3, 6.8, 7.1, 
      #
      #13, 12.7, 14,
      #11.3, 12.4, 11.4,
      
      4.9, 4.6, 5.,
      4.8, 4.5, 4.8,
      
      23.7, 23.2, 20.8,
      18.8, 18.4, 18.6
      
      #6.4, 5.2, 6.5,
      #5.3, 5., 5.9,
      
      #1.1, .9, 1.,
      #.9, .9, .9
      
    )
    
  )

Crawford2012_act_wide <- 
  Crawford2012 |> 
  filter(!str_detect(group, "Art")) |> 
  mutate(group = case_match(group, "Activity groups" ~ "t", "Standard care" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  ) |> 
  mutate(
    treatment = "Activity groups"
  ) |> 
  relocate(treatment)
  
Crawford2012_art_wide <- 
  Crawford2012 |> 
  filter(!str_detect(group, "Act")) |> 
  mutate(group = case_match(group, "Art therapy" ~ "t", "Standard care" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  ) |> 
  mutate(
    treatment = "Art therapy"
  ) |> 
  relocate(treatment)

Crawford2012_est <- 
  bind_rows(Crawford2012_act_wide, Crawford2012_art_wide) |> 
  arrange(outcome, timing) |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "Positive") ~ "All mental health outcomes/Symptoms of psychosis",
      str_detect(outcome, "function") ~ "Social functioning (degree of impairment)",
      outcome == "Wellbeing" ~ "Wellbeing and Quality of Life",
      TRUE ~ NA_character_
    ),
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Crawford et al. 2012",
    main_es_method = "Raw diff-in-diffs",
    
    ppcor = ppcor_imp,  
    ppcor_method = "Imputed",
    
    m_post = if_else(!str_detect(outcome, "Positive"), m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    m_diff_t = if_else(!str_detect(outcome, "Positive"), m_post_t - m_pre_t, (m_post_t - m_pre_t) * -1),
    m_diff_c = if_else(!str_detect(outcome, "Positive"), m_post_c - m_pre_c, (m_post_c - m_pre_c) * -1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # "Art therapy and activity groups had up to eight members 
    
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 1)",
    
    # Icc value
    icc = case_when(
      str_detect(outcome, "Global") & timing == 12 ~ 0.175,
      str_detect(outcome, "Global") & timing == 24 ~ 0.06,
      str_detect(outcome, "Positive") ~ 0.47,
      str_detect(outcome, "Social|Wellbeing") ~ ICC_01,
      TRUE ~ NA_real_
    ),
    
    icc_type = case_when(
      str_detect(outcome, "Global|Positive") ~ "From study (p. 3)",
      str_detect(outcome, "Social|Wellbeing") ~ "Imputed",
      TRUE ~ NA_character_
    ),
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
 
    # Cluster-adjusting g_post
    gt_post =  omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = vgt_post:Wgt_post
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * ((m_diff_t - m_diff_c)/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste(outcome, timing, treatment, sep = "/"),
  
    timing = as.character(timing)
    
  ) |> 
  ungroup(); Crawford2012_est
## # A tibble: 16 × 66
##    treatment       outcome                                timing N_start_c
##    <chr>           <chr>                                  <chr>      <dbl>
##  1 Activity groups Global assessment of functioning (GAF) 12           137
##  2 Art therapy     Global assessment of functioning (GAF) 12           137
##  3 Activity groups Global assessment of functioning (GAF) 24           137
##  4 Art therapy     Global assessment of functioning (GAF) 24           137
##  5 Activity groups Positive and negative syndrome scale   12           137
##  6 Art therapy     Positive and negative syndrome scale   12           137
##  7 Activity groups Positive and negative syndrome scale   24           137
##  8 Art therapy     Positive and negative syndrome scale   24           137
##  9 Activity groups Social functioning                     12           137
## 10 Art therapy     Social functioning                     12           137
## 11 Activity groups Social functioning                     24           137
## 12 Art therapy     Social functioning                     24           137
## 13 Activity groups Wellbeing                              12           137
## 14 Art therapy     Wellbeing                              12           137
## 15 Activity groups Wellbeing                              24           137
## 16 Art therapy     Wellbeing                              24           137
##    N_start_t   N_c   N_t m_pre_c m_pre_t sd_pre_c sd_pre_t m_post_c m_post_t
##        <dbl> <dbl> <dbl>   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
##  1       140   121   121    44.9    45       12.6     12.7     45.7     45.5
##  2       140   121   119    44.9    44.8     12.6     13.1     45.7     44.9
##  3       140   117   121    44.9    45       12.6     12.7     46.8     46.4
##  4       140   117   117    44.9    44.8     12.6     13.1     46.8     45.6
##  5       140   121   121    72.6    75.3     21.5     22       71.2     69.6
##  6       140   121   119    72.6    74.3     21.5     23.7     71.2     72.7
##  7       140   117   121    72.6    75.3     21.5     22       68.1     66.9
##  8       140   117   117    72.6    74.3     21.5     23.7     68.1     69.2
##  9       140   121   121     8.1     9        4.7      4.8      8.5      8.1
## 10       140   121   119     8.1     8.6      4.7      4.2      8.5      8.3
## 11       140   117   121     8.1     9        4.7      4.8      8.1      8  
## 12       140   117   117     8.1     8.6      4.7      4.2      8.1      8.2
## 13       140   121   121    64.5    59.1     20.6     19.5     64.1     63.6
## 14       140   121   119    64.5    58.3     20.6     21.1     64.1     59.6
## 15       140   117   121    64.5    59.1     20.6     19.5     68.1     66.1
## 16       140   117   117    64.5    58.3     20.6     21.1     68.1     65.1
##    sd_post_c sd_post_t analysis_plan                                    N_total
##        <dbl>     <dbl> <chr>                                              <dbl>
##  1      14.4      14.1 Social functioning (degree of impairment)            242
##  2      14.4      14.6 Social functioning (degree of impairment)            240
##  3      12.8      13.6 Social functioning (degree of impairment)            238
##  4      12.8      13.1 Social functioning (degree of impairment)            234
##  5      24.6      23.2 All mental health outcomes/Symptoms of psychosis     242
##  6      24.6      27.3 All mental health outcomes/Symptoms of psychosis     240
##  7      20.7      23.3 All mental health outcomes/Symptoms of psychosis     238
##  8      20.7      21.8 All mental health outcomes/Symptoms of psychosis     234
##  9       4.9       4.6 Social functioning (degree of impairment)            242
## 10       4.9       5   Social functioning (degree of impairment)            240
## 11       4.8       4.5 Social functioning (degree of impairment)            238
## 12       4.8       4.8 Social functioning (degree of impairment)            234
## 13      23.7      23.2 Wellbeing and Quality of Life                        242
## 14      23.7      20.8 Wellbeing and Quality of Life                        240
## 15      18.8      18.4 Wellbeing and Quality of Life                        238
## 16      18.8      18.6 Wellbeing and Quality of Life                        234
##    df_ind effect_size sd_used            study                main_es_method   
##     <dbl> <chr>       <chr>              <chr>                <chr>            
##  1    242 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
##  2    240 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
##  3    238 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
##  4    234 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
##  5    242 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
##  6    240 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
##  7    238 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
##  8    234 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
##  9    242 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
## 10    240 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
## 11    238 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
## 12    234 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
## 13    242 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
## 14    240 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
## 15    238 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
## 16    234 SMD         Pooled posttest SD Crawford et al. 2012 Raw diff-in-diffs
##    ppcor ppcor_method  m_post sd_pool   d_post vd_post Wd_post      J   g_post
##    <dbl> <chr>          <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>    <dbl>
##  1   0.5 Imputed      -0.2000  14.25  -0.01403 0.01653 0.01653 0.9969 -0.01399
##  2   0.5 Imputed      -0.8000  14.50  -0.05517 0.01667 0.01667 0.9969 -0.05500
##  3   0.5 Imputed      -0.4000  13.21  -0.03027 0.01681 0.01681 0.9968 -0.03018
##  4   0.5 Imputed      -1.200   12.95  -0.09266 0.01711 0.01709 0.9968 -0.09236
##  5   0.5 Imputed       1.600   23.91   0.06692 0.01654 0.01653 0.9969  0.06671
##  6   0.5 Imputed      -1.5     25.97  -0.05775 0.01667 0.01667 0.9969 -0.05757
##  7   0.5 Imputed       1.200   22.06   0.05440 0.01682 0.01681 0.9968  0.05422
##  8   0.5 Imputed      -1.100   21.26  -0.05175 0.01710 0.01709 0.9968 -0.05158
##  9   0.5 Imputed      -0.4000   4.752 -0.08417 0.01654 0.01653 0.9969 -0.08391
## 10   0.5 Imputed      -0.2000   4.950 -0.04041 0.01667 0.01667 0.9969 -0.04028
## 11   0.5 Imputed      -0.1000   4.650 -0.02151 0.01681 0.01681 0.9968 -0.02144
## 12   0.5 Imputed       0.1000   4.8    0.02083 0.01709 0.01709 0.9968  0.02077
## 13   0.5 Imputed      -0.5000  23.45  -0.02132 0.01653 0.01653 0.9969 -0.02125
## 14   0.5 Imputed      -4.500   22.31  -0.2017  0.01675 0.01667 0.9969 -0.2011 
## 15   0.5 Imputed      -2       18.60  -0.1075  0.01684 0.01681 0.9968 -0.1072 
## 16   0.5 Imputed      -3       18.70  -0.1604  0.01715 0.01709 0.9968 -0.1599 
##    vg_post Wg_post m_diff_t m_diff_c      d_DD   vd_DD   Wd_DD      g_DD   vg_DD
##      <dbl>   <dbl>    <dbl>    <dbl>     <dbl>   <dbl>   <dbl>     <dbl>   <dbl>
##  1 0.01653 0.01653   0.5      0.8000 -0.02105  0.01653 0.01653 -0.02099  0.01653
##  2 0.01667 0.01667   0.1000   0.8000 -0.04828  0.01667 0.01667 -0.04813  0.01667
##  3 0.01681 0.01681   1.400    1.900  -0.03784  0.01681 0.01681 -0.03772  0.01681
##  4 0.01711 0.01709   0.8000   1.900  -0.08494  0.01711 0.01709 -0.08466  0.01711
##  5 0.01654 0.01653   5.7      1.400   0.1798   0.01660 0.01653  0.1793   0.01660
##  6 0.01667 0.01667   1.600    1.400   0.007700 0.01667 0.01667  0.007676 0.01667
##  7 0.01682 0.01681   8.400    4.5     0.1768   0.01688 0.01681  0.1762   0.01688
##  8 0.01710 0.01709   5.100    4.5     0.02823  0.01710 0.01709  0.02814  0.01710
##  9 0.01654 0.01653  -0.9      0.4000 -0.2735   0.01668 0.01653 -0.2727   0.01668
## 10 0.01667 0.01667  -0.3000   0.4000 -0.1414   0.01671 0.01667 -0.1410   0.01671
## 11 0.01681 0.01681  -1        0      -0.2151   0.01691 0.01681 -0.2144   0.01691
## 12 0.01709 0.01709  -0.4000   0      -0.08333  0.01711 0.01709 -0.08307  0.01711
## 13 0.01653 0.01653   4.5     -0.4000  0.2089   0.01662 0.01653  0.2083   0.01662
## 14 0.01675 0.01667   1.300   -0.4000  0.07620  0.01668 0.01667  0.07596  0.01668
## 15 0.01684 0.01681   7.000    3.600   0.1828   0.01688 0.01681  0.1822   0.01688
## 16 0.01715 0.01709   6.8      3.600   0.1711   0.01716 0.01709  0.1706   0.01716
##      Wg_DD avg_cl_size avg_cl_type         icc icc_type          n_covariates
##      <dbl>       <dbl> <chr>             <dbl> <chr>                    <dbl>
##  1 0.01653           8 From study (p. 1) 0.175 From study (p. 3)            1
##  2 0.01667           8 From study (p. 1) 0.175 From study (p. 3)            1
##  3 0.01681           8 From study (p. 1) 0.06  From study (p. 3)            1
##  4 0.01709           8 From study (p. 1) 0.06  From study (p. 3)            1
##  5 0.01653           8 From study (p. 1) 0.47  From study (p. 3)            1
##  6 0.01667           8 From study (p. 1) 0.47  From study (p. 3)            1
##  7 0.01681           8 From study (p. 1) 0.47  From study (p. 3)            1
##  8 0.01709           8 From study (p. 1) 0.47  From study (p. 3)            1
##  9 0.01653           8 From study (p. 1) 0.1   Imputed                      1
## 10 0.01667           8 From study (p. 1) 0.1   Imputed                      1
## 11 0.01681           8 From study (p. 1) 0.1   Imputed                      1
## 12 0.01709           8 From study (p. 1) 0.1   Imputed                      1
## 13 0.01653           8 From study (p. 1) 0.1   Imputed                      1
## 14 0.01667           8 From study (p. 1) 0.1   Imputed                      1
## 15 0.01681           8 From study (p. 1) 0.1   Imputed                      1
## 16 0.01709           8 From study (p. 1) 0.1   Imputed                      1
##    gamma_sqrt df_adj  omega  gt_post vgt_post Wgt_post     gt_DD  vgt_DD  Wgt_DD
##         <dbl>  <dbl>  <dbl>    <dbl>    <dbl>    <dbl>     <dbl>   <dbl>   <dbl>
##  1      0.953 212.0  0.9965 -0.01333  0.02521  0.02521 -0.01999  0.02521 0.02521
##  2      0.952 210.4  0.9964 -0.05234  0.02552  0.02552 -0.04580  0.02552 0.02552
##  3      0.984 232.8  0.9968 -0.02969  0.01977  0.01977 -0.03712  0.01977 0.01977
##  4      0.984 228.9  0.9967 -0.09088  0.02019  0.02017 -0.08330  0.02019 0.02017
##  5      0.867 100.5  0.9925  0.05758  0.03985  0.03983  0.1548   0.03995 0.03983
##  6      0.866  99.88 0.9925 -0.04964  0.04045  0.04044  0.006618 0.04044 0.04044
##  7      0.869  98.52 0.9924  0.04691  0.03999  0.03998  0.1525   0.04010 0.03998
##  8      0.866  97.22 0.9923 -0.04447  0.04121  0.04120  0.02425  0.04120 0.04120
##  9      0.973 230.9  0.9967 -0.08163  0.02150  0.02149 -0.2653   0.02164 0.02149
## 10      0.973 229.0  0.9967 -0.03919  0.02172  0.02172 -0.1371   0.02176 0.02172
## 11      0.974 226.9  0.9967 -0.02088  0.02174  0.02174 -0.2088   0.02183 0.02174
## 12      0.973 223.2  0.9966  0.02020  0.02222  0.02222 -0.08081  0.02224 0.02222
## 13      0.973 230.9  0.9967 -0.02068  0.02149  0.02149  0.2026   0.02158 0.02149
## 14      0.973 229.0  0.9967 -0.1956   0.02180  0.02172  0.07390  0.02173 0.02172
## 15      0.974 226.9  0.9967 -0.1044   0.02176  0.02174  0.1775   0.02181 0.02174
## 16      0.973 223.2  0.9966 -0.1556   0.02228  0.02222  0.1659   0.02228 0.02222
##        hg_DD   vhg_DD   h_DD  df_DD n_covariates_DD adj_fct_DD adj_value_DD
##        <dbl>    <dbl>  <dbl>  <dbl>           <dbl> <chr>             <dbl>
##  1 -0.008647 0.004716 212.0  212.0                1 eta               1.525
##  2 -0.01976  0.004752 210.4  210.4                1 eta               1.531
##  3 -0.01730  0.004296 232.8  232.8                1 eta               1.176
##  4 -0.03876  0.004369 228.9  228.9                1 eta               1.18 
##  5  0.07729  0.009947 100.5  100.5                1 eta               2.41 
##  6  0.003293 0.01001   99.88  99.88               1 eta               2.426
##  7  0.07678  0.01015   98.52  98.52               1 eta               2.378
##  8  0.01212  0.01029   97.22  97.22               1 eta               2.41 
##  9 -0.1190   0.004331 230.9  230.9                1 eta               1.3  
## 10 -0.06148  0.004366 229.0  229.0                1 eta               1.303
## 11 -0.09394  0.004407 226.9  226.9                1 eta               1.293
## 12 -0.03628  0.004480 223.2  223.2                1 eta               1.3  
## 13  0.09092  0.004331 230.9  230.9                1 eta               1.3  
## 14  0.03313  0.004366 229.0  229.0                1 eta               1.303
## 15  0.07987  0.004407 226.9  226.9                1 eta               1.293
## 16  0.07448  0.004480 223.2  223.2                1 eta               1.3  
##    gt_DD_pop vgt_DD_pop Wgt_DD_pop
##        <dbl>      <dbl>      <dbl>
##  1  -0.02237    0.02409    0.02409
##  2  -0.05214    0.02439    0.02439
##  3  -0.03851    0.01890    0.01889
##  4  -0.08471    0.01929    0.01928
##  5  NA         NA         NA      
##  6  NA         NA         NA      
##  7  NA         NA         NA      
##  8  NA         NA         NA      
##  9  NA         NA         NA      
## 10  NA         NA         NA      
## 11  NA         NA         NA      
## 12  NA         NA         NA      
## 13  NA         NA         NA      
## 14  NA         NA         NA      
## 15  NA         NA         NA      
## 16  NA         NA         NA      
##    vary_id                                                  
##    <chr>                                                    
##  1 Global assessment of functioning (GAF)/12/Activity groups
##  2 Global assessment of functioning (GAF)/12/Art therapy    
##  3 Global assessment of functioning (GAF)/24/Activity groups
##  4 Global assessment of functioning (GAF)/24/Art therapy    
##  5 Positive and negative syndrome scale/12/Activity groups  
##  6 Positive and negative syndrome scale/12/Art therapy      
##  7 Positive and negative syndrome scale/24/Activity groups  
##  8 Positive and negative syndrome scale/24/Art therapy      
##  9 Social functioning/12/Activity groups                    
## 10 Social functioning/12/Art therapy                        
## 11 Social functioning/24/Activity groups                    
## 12 Social functioning/24/Art therapy                        
## 13 Wellbeing/12/Activity groups                             
## 14 Wellbeing/12/Art therapy                                 
## 15 Wellbeing/24/Activity groups                             
## 16 Wellbeing/24/Art therapy
rm(Crawford2012_act_wide, Crawford2012_art_wide)

Druss et al. (2010) (Quality-checked)

Entering data from Table 2 (p. 268)

Druss2010 <- tibble(
  
  outcome = rep(c(
    # "patient_activation", # unused outcomes
    # "Physical_activity",
    # "medication_adherence",
    "QoL_physical",
    "QoL_mental"
  ), each = 2),
  
  group = rep(c(
    "HARP",
    "TAU"
  ), 2),
  
  N = rep(c(
    41,39
  ), 2),
  
  N_start = rep(c(
    41,39
  ), 2),
  
  m_pre = c(
    #48.3, 47.6, # patient activation
    #150, 154, # Physical_activity
    #1.5, 1.5, # medication_adherence
    36.9, 37.0, # QoL_physical
    33.3, 33.9 # QoL_mental
  ),
  
  sd_pre = c(
    #11.5, 12.3,
    #236, 194,
    #1.2, 1.4,
    10.3, 12.5,
    9.13, 9.3
  ),
  
  m_post = c(
    #52.0, 44.9,
    #191, 152,
    #1.3, 1.6,
    42.9, 40,
    36.8, 37.0
  ),
  
  sd_post = c(
    #10.1, 9.6,
    #278, 249,
    #1.3, 1.4,
    14.2, 13.7,
    10.0, 11.8
  )
  
)

Druss2010_est <- 
  Druss2010 |> 
  mutate(group = case_match(group, "HARP" ~ "t", "TAU" ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
  ) |> 
  mutate(
    
    analysis_plan = "Wellbeing and Quality of Life",
    
    N_total = N_t + N_c,
    
    p_val_f = c(
      #0.03, 0.4, 0.22, 
      0.41, 0.96
      ),
    
    # Not used - seems flawed
    F_val = qf(p_val_f, df1 = 1, df2 = N_total - 2, lower.tail = FALSE),
    
    ppcor = ppcor_imp,  
    ppcor_method = "Imputed",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Druss et al. 2010",
    main_es_method = "Raw diff-in-diffs",
   
    df_ind = N_total,
    
    m_post = if_else(outcome != "medication_adherence", m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    m_diff_t = if_else(outcome != "medication_adherence", m_post_t - m_pre_t, (m_post_t - m_pre_t) * -1),
    m_diff_c = if_else(outcome != "medication_adherence", m_post_c - m_pre_c, (m_post_c - m_pre_c) * -1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # We don't know the average group size. Therefore, we impute this value
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    ppcor_method = "F value used",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),

    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    

    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Druss2010_est
## # A tibble: 2 × 63
##   outcome        N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t sd_pre_c
##   <chr>        <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 QoL_physical    41    39        41        39    36.9    37      10.3      12.5
## 2 QoL_mental      41    39        41        39    33.3    33.9     9.13      9.3
##   m_post_t m_post_c sd_post_t sd_post_c analysis_plan                 N_total
##      <dbl>    <dbl>     <dbl>     <dbl> <chr>                           <dbl>
## 1     42.9       40      14.2      13.7 Wellbeing and Quality of Life      80
## 2     36.8       37      10        11.8 Wellbeing and Quality of Life      80
##   p_val_f    F_val ppcor ppcor_method effect_size sd_used           
##     <dbl>    <dbl> <dbl> <chr>        <chr>       <chr>             
## 1    0.41 0.6862     0.5 F value used SMD         Pooled posttest SD
## 2    0.96 0.002532   0.5 F value used SMD         Pooled posttest SD
##   study             main_es_method    df_ind  m_post sd_pool   d_post vd_post
##   <chr>             <chr>              <dbl>   <dbl>   <dbl>    <dbl>   <dbl>
## 1 Druss et al. 2010 Raw diff-in-diffs     80  2.9      13.96  0.2078  0.05030
## 2 Druss et al. 2010 Raw diff-in-diffs     80 -0.2000   10.91 -0.01832 0.05003
##   Wd_post      J   g_post vg_post Wg_post m_diff_t m_diff_c    d_DD   vd_DD
##     <dbl>  <dbl>    <dbl>   <dbl>   <dbl>    <dbl>    <dbl>   <dbl>   <dbl>
## 1 0.05003 0.9906  0.2058  0.05030 0.05003      6        3   0.2149  0.05032
## 2 0.05003 0.9906 -0.01815 0.05003 0.05003      3.5      3.1 0.03665 0.05004
##     Wd_DD    g_DD   vg_DD   Wg_DD avg_cl_size avg_cl_type   icc icc_type
##     <dbl>   <dbl>   <dbl>   <dbl>       <dbl> <chr>       <dbl> <chr>   
## 1 0.05003 0.2129  0.05031 0.05003           8 Imputed       0.1 Imputed 
## 2 0.05003 0.03631 0.05004 0.05003           8 Imputed       0.1 Imputed 
##   n_covariates gamma_sqrt df_adj  omega  gt_post vgt_post Wgt_post   gt_DD
##          <dbl>      <dbl>  <dbl>  <dbl>    <dbl>    <dbl>    <dbl>   <dbl>
## 1            1      0.971  75.28 0.9900  0.1997   0.06481  0.06454 0.2066 
## 2            1      0.971  75.28 0.9900 -0.01762  0.06454  0.06454 0.03523
##    vgt_DD  Wgt_DD   hg_DD  vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD
##     <dbl>   <dbl>   <dbl>   <dbl> <dbl> <dbl>           <dbl> <chr>     
## 1 0.06482 0.06454 0.09366 0.01328 75.28 75.28               1 eta       
## 2 0.06455 0.06454 0.01598 0.01328 75.28 75.28               1 eta       
##   adj_value_DD vary_id     
##          <dbl> <chr>       
## 1         1.29 QoL_physical
## 2         1.29 QoL_mental

Druss et al. (2018) (Quality-checked)

Data from Table 2 (p. 532), containing means and SD for health related quality of life (PCS and MCS),and Table 3 (p. 533), containing means and SD for RAS

# Data from table 2 (p. 532), containing means and SD for health related quality of life 
# (PCS and MCS),and table 3 (p. 533), containing means and SD for RAS

Druss2018 <- tibble(
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,6)),
  
  timing = rep(c("3m", "6m"), each = 6,1),
  
  outcome = as.factor(rep(c("RAS", # Recovery Assessment Scale
                  # Both of the below measures is related to health related quality of life
                  # as measured by the Short-Form Health Survey (SF-36) (see p. 531)
                        "PCS", # The physical component summary
                        "MCS"), # The mental component summary
                        each= 2,2)),
  
  N = rep(c(198, 202), each = 1,6),
  
  N_start = rep(c(198, 202), each = 1,6),
 
 
 m_pre = rep(c(
   3.72, 3.66,   # RAS
   32.73, 32.74, # PCS
   32.05, 32.04) # MCS
 , each = 1,2)
 ,
  
  sd_pre = rep(c(
    0.62, 0.57,    # RAS
    10.92, 11.29,  # PCS
    11.79, 11.36), # MCS
    each = 1,2) 
 ,
  
  m_post = c(
    3.89, 3.70,   # RAS 3 months
    34.49, 33.89, # PCS 3 months
    34.49, 34.25, # MCS 3 months
    
    3.87, 3.74,   # RAS 6 months
    35.42, 34.25, # PCS 6 months
    36.64, 34.54  # MCS 6 months
  ),
  
  sd_post = c(
    0.55, 0.68,   # RAS 3 months
    11.15, 10.41, # PCS 3 months
    11.15, 11.97, # MCS 3 months
    
    0.61, 0.59,   # RAS 6 months
    11.02, 11.52, # PCS 6 months
    12.28, 11.82  # MCS 6 months
  )
) |> 
#arrange(outcome) |> 
relocate(outcome, .after = group); Druss2018
## # A tibble: 12 × 9
##    group        outcome timing     N N_start m_pre sd_pre m_post sd_post
##    <fct>        <fct>   <chr>  <dbl>   <dbl> <dbl>  <dbl>  <dbl>   <dbl>
##  1 Intervention RAS     3m       198     198  3.72   0.62   3.89    0.55
##  2 Control      RAS     3m       202     202  3.66   0.57   3.7     0.68
##  3 Intervention PCS     3m       198     198 32.73  10.92  34.49   11.15
##  4 Control      PCS     3m       202     202 32.74  11.29  33.89   10.41
##  5 Intervention MCS     3m       198     198 32.05  11.79  34.49   11.15
##  6 Control      MCS     3m       202     202 32.04  11.36  34.25   11.97
##  7 Intervention RAS     6m       198     198  3.72   0.62   3.87    0.61
##  8 Control      RAS     6m       202     202  3.66   0.57   3.74    0.59
##  9 Intervention PCS     6m       198     198 32.73  10.92  35.42   11.02
## 10 Control      PCS     6m       202     202 32.74  11.29  34.25   11.52
## 11 Intervention MCS     6m       198     198 32.05  11.79  36.64   12.28
## 12 Control      MCS     6m       202     202 32.04  11.36  34.54   11.82
# Making the druss2018 tibble wide in order to estimate the effect sizes and
# further analysis. 

# Turning data into wide format
Druss2018_wide <- 
  Druss2018 |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )   

Druss2018_est <- 
  Druss2018_wide |> 
  # Based on the degrees of freedom value reported in Druss et al. 2018 table 2 and 3
  mutate(
     analysis_plan = case_when(
      str_detect(outcome, "RAS") ~ "Hope, Empowerment & Self-efficacy",
      str_detect(outcome, "PCS|MCS") ~ "Wellbeing and Quality of Life",
      .default = NA_character_
    ),
    
    study = "Druss et al. 2018",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    ppcor = ppcor_imp, # Imputed ppcor
    ppcor_method = "Imputed",
    
  ) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    m_post =  (m_post_t - m_post_c),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    m_diff_t = m_post_t - m_pre_t,
    m_diff_c = m_post_c - m_pre_c,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) ,
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD,
    
    
  ) |>  
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # Average cluster size in treatment group
    # Harp intevention had six to ten partcipants 
    
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 530)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    )
    
  ) |> 
  ungroup() |> 
  mutate(vary_id = paste0(outcome, "/", timing)
  ); Druss2018_est
## # A tibble: 6 × 62
##   outcome timing   N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t
##   <fct>   <chr>  <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>
## 1 RAS     3m       198   202       198       202    3.72    3.66     0.62
## 2 PCS     3m       198   202       198       202   32.73   32.74    10.92
## 3 MCS     3m       198   202       198       202   32.05   32.04    11.79
## 4 RAS     6m       198   202       198       202    3.72    3.66     0.62
## 5 PCS     6m       198   202       198       202   32.73   32.74    10.92
## 6 MCS     6m       198   202       198       202   32.05   32.04    11.79
##   sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
## 1     0.57     3.89     3.7       0.55      0.68
## 2    11.29    34.49    33.89     11.15     10.41
## 3    11.36    34.49    34.25     11.15     11.97
## 4     0.57     3.87     3.74      0.61      0.59
## 5    11.29    35.42    34.25     11.02     11.52
## 6    11.36    36.64    34.54     12.28     11.82
##   analysis_plan                     study             effect_size
##   <chr>                             <chr>             <chr>      
## 1 Hope, Empowerment & Self-efficacy Druss et al. 2018 SMD        
## 2 Wellbeing and Quality of Life     Druss et al. 2018 SMD        
## 3 Wellbeing and Quality of Life     Druss et al. 2018 SMD        
## 4 Hope, Empowerment & Self-efficacy Druss et al. 2018 SMD        
## 5 Wellbeing and Quality of Life     Druss et al. 2018 SMD        
## 6 Wellbeing and Quality of Life     Druss et al. 2018 SMD        
##   sd_used            main_es_method    ppcor ppcor_method N_total df_ind m_post
##   <chr>              <chr>             <dbl> <chr>          <dbl>  <dbl>  <dbl>
## 1 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          400    400 0.19  
## 2 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          400    400 0.6000
## 3 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          400    400 0.2400
## 4 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          400    400 0.1300
## 5 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          400    400 1.170 
## 6 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          400    400 2.100 
##   sd_pool  d_post vd_post Wd_post      J  g_post vg_post Wg_post m_diff_t
##     <dbl>   <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>    <dbl>
## 1  0.6191 0.3069  0.01012 0.01000 0.9981 0.3063  0.01012 0.01000   0.17  
## 2 10.78   0.05565 0.01000 0.01000 0.9981 0.05554 0.01000 0.01000   1.760 
## 3 11.57   0.02074 0.01000 0.01000 0.9981 0.02070 0.01000 0.01000   2.440 
## 4  0.6000 0.2167  0.01006 0.01000 0.9981 0.2163  0.01006 0.01000   0.1500
## 5 11.28   0.1038  0.01001 0.01000 0.9981 0.1036  0.01001 0.01000   2.690 
## 6 12.05   0.1743  0.01004 0.01000 0.9981 0.1739  0.01004 0.01000   4.590 
##   m_diff_c    d_DD   vd_DD   Wd_DD    g_DD   vg_DD   Wg_DD avg_cl_size
##      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>       <dbl>
## 1  0.04000 0.2100  0.01006 0.01000 0.2096  0.01006 0.01000           8
## 2  1.150   0.05657 0.01001 0.01000 0.05647 0.01000 0.01000           8
## 3  2.21    0.01988 0.01000 0.01000 0.01984 0.01000 0.01000           8
## 4  0.08000 0.1167  0.01002 0.01000 0.1165  0.01002 0.01000           8
## 5  1.510   0.1047  0.01001 0.01000 0.1045  0.01001 0.01000           8
## 6  2.5     0.1734  0.01004 0.01000 0.1731  0.01004 0.01000           8
##   avg_cl_type           icc icc_type n_covariates gamma_sqrt df_adj  omega
##   <chr>               <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
## 1 From study (p. 530)   0.1 Imputed             1      0.974  382.7 0.9980
## 2 From study (p. 530)   0.1 Imputed             1      0.974  382.7 0.9980
## 3 From study (p. 530)   0.1 Imputed             1      0.974  382.7 0.9980
## 4 From study (p. 530)   0.1 Imputed             1      0.974  382.7 0.9980
## 5 From study (p. 530)   0.1 Imputed             1      0.974  382.7 0.9980
## 6 From study (p. 530)   0.1 Imputed             1      0.974  382.7 0.9980
##   gt_post vgt_post Wgt_post   gt_DD  vgt_DD  Wgt_DD    hg_DD   vhg_DD  h_DD
##     <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>    <dbl>    <dbl> <dbl>
## 1 0.2983   0.01316  0.01304 0.2041  0.01310 0.01304 0.09131  0.002613 382.7
## 2 0.05409  0.01305  0.01304 0.05499 0.01305 0.01304 0.02462  0.002613 382.7
## 3 0.02016  0.01304  0.01304 0.01932 0.01304 0.01304 0.008649 0.002613 382.7
## 4 0.2106   0.01310  0.01304 0.1134  0.01306 0.01304 0.05076  0.002613 382.7
## 5 0.1009   0.01305  0.01304 0.1017  0.01305 0.01304 0.04553  0.002613 382.7
## 6 0.1694   0.01308  0.01304 0.1686  0.01308 0.01304 0.07544  0.002613 382.7
##   df_DD n_covariates_DD adj_fct_DD adj_value_DD vary_id
##   <dbl>           <dbl> <chr>             <dbl> <chr>  
## 1 382.7               1 eta               1.304 RAS/3m 
## 2 382.7               1 eta               1.304 PCS/3m 
## 3 382.7               1 eta               1.304 MCS/3m 
## 4 382.7               1 eta               1.304 RAS/6m 
## 5 382.7               1 eta               1.304 PCS/6m 
## 6 382.7               1 eta               1.304 MCS/6m

Dyck et al. (2000) (Quality-checked)

Data from table 2 (p. 516) containing means and standard deviation

Dyck2000 <- tibble(
  group = as.factor(c("Multiple-family group", 
                          "Standard care")),
  outcome = "MSANS", #The modified Scale for the Assessment of Negative Symptoms
  N = 21,
  
  N_start = 21,
  
  m_pre = c(
    7.9, 8.7
  ),
  
  m_post = c(
#    7.4, 9.1, # 3 months
#    7.2, 8.9, # 6 months
#    7.2, 8.9, # 9 months
    7.2, 8.4 # 12 months, the real post-treatment measure because the treatment
             # ends after 12 months.
  ),
  
  
  sd_pre = c(
    3.1, 3.3 
  ),
  
  sd_post = c(
#    2.3, 3.2, # 3 months
#    2.1, 2.7, # 6 months
#    2.1, 3,   # 9 months
    2, 3.1    # 12 months, the real post-treatment measure because the treatment
              # ends after 12 months.
  )
)



# Making the dyck2000 tibble wide in order to estimate the effect sizes and
# further analysis. 
Dyck2000_wide <-
  Dyck2000 |> 
  mutate (group = case_match(
    group, "Multiple-family group"  ~ "t", "Standard care" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )

# Effect size calculating 
Dyck2000_est <-           
  Dyck2000_wide |>
  mutate(
    analysis_plan = "All mental health outcomes/Negative symptoms",
    
    F_val = 6.1,
  
    study = "Dyck et al. 2000",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
  # The outcome MSANS reverted because lower score is beneficial
    m_post =  (m_post_t - m_post_c)*-1,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
  
  # The outcome MSANS reverted because lower score is beneficial
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = d_DD^2/F_val + d_DD^2/(2*df_ind),
    Wd_DD = d_DD^2/F_val,
    
    g_DD = J * d_DD, 
    vg_DD = g_DD^2/F_val + g_DD^2/(2*df_ind),
    Wg_DD = g_DD^2/F_val
  
  ) |> 
  rowwise() |> 
  mutate(
  
  # Average cluster size in treatment group
  
  avg_cl_size = 7, 
  avg_cl_type = "From study (p. 516)",
  
  # Imputed icc value
  icc = ICC_01,
  icc_type = "Imputed",
  
  ppcor_method = "F value used",
  
  n_covariates = 1,
  
  # Find info about the function via ?VIVECampbell::gamma_1armcluster
  gamma_sqrt = VIVECampbell::gamma_1armcluster(
    N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
  ),
  
  # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
  df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
  omega = 1 - 3/(4*df_adj-1),
  
  
  # Cluster-adjusting g_post
  gt_post = omega * d_post * gamma_sqrt,
  VIVECampbell::vgt_smd_1armcluster(
    N_cl_grp = N_t, 
    N_ind_grp = N_c, 
    avg_grp_size = avg_cl_size, 
    ICC = icc, 
    g = gt_post, 
    model = "posttest",
    cluster_adj = FALSE,
    add_name_to_vars = "_post",
    vars = c(vgt_post, Wgt_post)
  ),
  
  gt_DD = omega * d_DD * gamma_sqrt,
  VIVECampbell::vgt_smd_1armcluster(
    N_cl_grp = N_t, 
    N_ind_grp = N_c, 
    avg_grp_size = avg_cl_size, 
    ICC = icc, 
    g = gt_DD, 
    model = "DiD",
    cluster_adj = FALSE,
    F_val = F_val,
    q = n_covariates,
    add_name_to_vars = "_DD",
    vars = -var_term1_DD)

  
) |> 
ungroup() |> 
mutate(vary_id = outcome); Dyck2000_est
## # A tibble: 1 × 61
##   outcome   N_t   N_c N_start_t N_start_c m_pre_t m_pre_c m_post_t m_post_c
##   <chr>   <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 MSANS      21    21        21        21     7.9     8.7      7.2      8.4
##   sd_pre_t sd_pre_c sd_post_t sd_post_c
##      <dbl>    <dbl>     <dbl>     <dbl>
## 1      3.1      3.3         2       3.1
##   analysis_plan                                F_val study           
##   <chr>                                        <dbl> <chr>           
## 1 All mental health outcomes/Negative symptoms   6.1 Dyck et al. 2000
##   effect_size sd_used            main_es_method    N_total df_ind m_post sd_pool
##   <chr>       <chr>              <chr>               <dbl>  <dbl>  <dbl>   <dbl>
## 1 SMD         Pooled posttest SD Raw diff-in-diffs      42     42    1.2   2.609
##   d_post vd_post Wd_post      J g_post vg_post Wg_post m_diff_t m_diff_c   d_DD
##    <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>  <dbl>
## 1 0.4600 0.09776 0.09524 0.9820 0.4517 0.09767 0.09524      0.7   0.3000 0.1533
##      vd_DD    Wd_DD   g_DD    vg_DD    Wg_DD avg_cl_size avg_cl_type        
##      <dbl>    <dbl>  <dbl>    <dbl>    <dbl>       <dbl> <chr>              
## 1 0.004134 0.003854 0.1506 0.003987 0.003717           7 From study (p. 516)
##     icc icc_type ppcor_method n_covariates gamma_sqrt df_adj  omega gt_post
##   <dbl> <chr>    <chr>               <dbl>      <dbl>  <dbl>  <dbl>   <dbl>
## 1   0.1 Imputed  F value used            1      0.967  38.96 0.9806  0.4362
##   vgt_post Wgt_post  gt_DD   vgt_DD   Wgt_DD  hg_DD  vhg_DD  h_DD df_DD
##      <dbl>    <dbl>  <dbl>    <dbl>    <dbl>  <dbl>   <dbl> <dbl> <dbl>
## 1   0.1215   0.1190 0.1454 0.004604 0.004332 0.3503 0.02567 38.96 38.96
##   n_covariates_DD adj_fct_DD adj_value_DD vary_id
##             <dbl> <chr>             <dbl> <chr>  
## 1               1 eta                1.25 MSANS

van Gestel-Timmermans et al. (2012) (Quality-checked)

Missing data analysis to support the RoB 2 D4 assessment

# Testing missing 

Timmermans_sample_size_dat <-
  tibble(
    
    outcome = rep(c("Empoverment", "Hope", "Quality", "Self-e", "Loneliness"), each = 4),
    timing = rep(c("3 months", "6 months"), each = 2, n_distinct(outcome)),
    #measure = rep(c("post", "followup"), each = 2, n_distinct(outcome)),
    group = rep(c("Treatment", "Control"), 10),
    
    N_start = rep(rep(c(168, 165), 10)),
    
    N_actual = c(
      136, 117, 121, 99,
      132, 118, 120, 97,
      124, 114, 111, 97,
      134, 116, 121, 100,
      138, 122, 125, 102
    )
    
); Timmermans_sample_size_dat
## # A tibble: 20 × 5
##    outcome     timing   group     N_start N_actual
##    <chr>       <chr>    <chr>       <dbl>    <dbl>
##  1 Empoverment 3 months Treatment     168      136
##  2 Empoverment 3 months Control       165      117
##  3 Empoverment 6 months Treatment     168      121
##  4 Empoverment 6 months Control       165       99
##  5 Hope        3 months Treatment     168      132
##  6 Hope        3 months Control       165      118
##  7 Hope        6 months Treatment     168      120
##  8 Hope        6 months Control       165       97
##  9 Quality     3 months Treatment     168      124
## 10 Quality     3 months Control       165      114
## 11 Quality     6 months Treatment     168      111
## 12 Quality     6 months Control       165       97
## 13 Self-e      3 months Treatment     168      134
## 14 Self-e      3 months Control       165      116
## 15 Self-e      6 months Treatment     168      121
## 16 Self-e      6 months Control       165      100
## 17 Loneliness  3 months Treatment     168      138
## 18 Loneliness  3 months Control       165      122
## 19 Loneliness  6 months Treatment     168      125
## 20 Loneliness  6 months Control       165      102

Missingness analysis (used to conduct risk of bias assessement)

Timmermans_missingness <- 
  Timmermans_sample_size_dat |> 
  summarise(
    N_start_total = sum(N_start),
    N_actual_total = sum(N_actual),
    
    #percent_missing_total = round(mean(percent_missing)),
    
    percent_missing_total = round((1 - (N_actual_total/N_start_total)) * 100),
    
    RoB_D3_assess = case_when(
      percent_missing_total >= 25 ~ "High",
      percent_missing_total < 25 ~ "Some concerns",
      TRUE ~ NA_character_
    ),
    
    .by = c(outcome, timing)
  )

Timmermans_missingness 
## # A tibble: 10 × 6
##    outcome     timing   N_start_total N_actual_total percent_missing_total
##    <chr>       <chr>            <dbl>          <dbl>                 <dbl>
##  1 Empoverment 3 months           333            253                    24
##  2 Empoverment 6 months           333            220                    34
##  3 Hope        3 months           333            250                    25
##  4 Hope        6 months           333            217                    35
##  5 Quality     3 months           333            238                    29
##  6 Quality     6 months           333            208                    38
##  7 Self-e      3 months           333            250                    25
##  8 Self-e      6 months           333            221                    34
##  9 Loneliness  3 months           333            260                    22
## 10 Loneliness  6 months           333            227                    32
##    RoB_D3_assess
##    <chr>        
##  1 Some concerns
##  2 High         
##  3 High         
##  4 High         
##  5 High         
##  6 High         
##  7 High         
##  8 High         
##  9 Some concerns
## 10 High

Data is retrieved from Tables 2 & 3 (p. 58)

Timmermans2012 <- 
  Timmermans_sample_size_dat |> 
  rename(N =  N_actual) |> 
  mutate(
    
    #From Table 2 (p. 58)
    
    m_pre = c(
      rep(c(3.40, 3.37), 2), # Empowerment
      rep(c(2.78, 2.76), 2), # Hope
      rep(c(4.32, 4.23), 2), # Quality of life
      rep(c(4.38, 4.33), 2), # Self-efficacy beliefs
      rep(c(6.40, 6.87), 2)  # Loneliness
    ),
    
    sd_pre = c(
      rep(c(0.49, 0.51), 2), # Empowerment
      rep(c(0.47, 0.48), 2), # Hope
      rep(c(0.88, 1), 2),    # Quality of life
      rep(c(0.82, 0.89), 2), # Self-efficacy beliefs
      rep(c(3.56, 3.40), 2)  # Loneliness
    ),
    
    m_post = c(
      
      3.55, 3.38, 3.59, 3.40, # Empowerment
      2.91, 2.79, 2.97, 2.73, # Hope
      4.49, 4.36, 4.63, 4.39, # Quality of life
      4.65, 4.35, 4.71, 4.4,  # Self-efficacy beliefs
      5.89, 6.27, 3.87, 3.68  # Loneliness
      
    ),
    
    sd_post = c(
      
      0.48, 0.53, 0.50, 0.56,
      0.47, 0.53, 0.46, 0.48,
      0.96, 1.07, 0.97, 1.05,
      0.81, 0.97, 0.93, 0.88,
      3.61, 3.55, 3.87, 3.68
      
    ),
    
    es_paper = rep(
      c(
        0.27, 0.36, 
        0.25, 0.48,
        0.03, 0.15,
        0.27, 0.23, 
        -0.04, 0.15
      ),
      each = 2
    ),
    
    # From Table 3 (p. 58)
    
    b_adj = c(
      .12, .021, .13, .046,   #Empowerment
      .10, .038, .17, .004,   #Hope
      .064, .11, .11, .15,    #Quality of life
      .23, .064, .21, .14,    #Self-efficacy beliefs
      .066, -.65, -.34, -.49  #Loneliness
      
    ),
    
    pval_b_adj = c(
      .016, .55, .012, .24,  #Empowerment
      .039, .28, .001, .91,  #Hope
      .471, .09, .24, .025,  #Quality of life
      .010, .31, .026, .042, #Self-efficacy beliefs
      .85, .008, .35, .07    #Loneliness
      
      ),
    
    ci_l_b_adj = c(
      .022, -.05,  #Empowerment Treatment T1, Empowerment Control T1 
      .026, -.025, #Empowerment Treatment T2, Empowerment Control T2
      NA, -.030, 
      .026, -.025,
      -.11, -.016, 
      -.071, 0.020,
      .059, -.062, 
      .027, .006,
      -.60, -1.14, 
      -1.05, -1.02
    ),
    
    ci_u_b_adj = c(
      .22, .092, #Empowerment Treatment T1, Empowerment Control T1 
      .23, .12, #Empowerment Treatment T2, Empowerment Control T2 
      NA, .11, 
      .23, .12, 
      .23, .24, 
      .29, .28,
      .40, .19, 
      .39, .27,
      .74, -.16, 
      .37, .042
    ),
    
    tval_b_adj = qt(pval_b_adj/2, df = N-2, lower.tail = FALSE),
    se_b_adj = b_adj/tval_b_adj
    
  ); Timmermans2012 
## # A tibble: 20 × 16
##    outcome     timing   group     N_start     N m_pre sd_pre m_post sd_post
##    <chr>       <chr>    <chr>       <dbl> <dbl> <dbl>  <dbl>  <dbl>   <dbl>
##  1 Empoverment 3 months Treatment     168   136  3.4    0.49   3.55    0.48
##  2 Empoverment 3 months Control       165   117  3.37   0.51   3.38    0.53
##  3 Empoverment 6 months Treatment     168   121  3.4    0.49   3.59    0.5 
##  4 Empoverment 6 months Control       165    99  3.37   0.51   3.4     0.56
##  5 Hope        3 months Treatment     168   132  2.78   0.47   2.91    0.47
##  6 Hope        3 months Control       165   118  2.76   0.48   2.79    0.53
##  7 Hope        6 months Treatment     168   120  2.78   0.47   2.97    0.46
##  8 Hope        6 months Control       165    97  2.76   0.48   2.73    0.48
##  9 Quality     3 months Treatment     168   124  4.32   0.88   4.49    0.96
## 10 Quality     3 months Control       165   114  4.23   1      4.36    1.07
## 11 Quality     6 months Treatment     168   111  4.32   0.88   4.63    0.97
## 12 Quality     6 months Control       165    97  4.23   1      4.39    1.05
## 13 Self-e      3 months Treatment     168   134  4.38   0.82   4.65    0.81
## 14 Self-e      3 months Control       165   116  4.33   0.89   4.35    0.97
## 15 Self-e      6 months Treatment     168   121  4.38   0.82   4.71    0.93
## 16 Self-e      6 months Control       165   100  4.33   0.89   4.4     0.88
## 17 Loneliness  3 months Treatment     168   138  6.4    3.56   5.89    3.61
## 18 Loneliness  3 months Control       165   122  6.87   3.4    6.27    3.55
## 19 Loneliness  6 months Treatment     168   125  6.4    3.56   3.87    3.87
## 20 Loneliness  6 months Control       165   102  6.87   3.4    3.68    3.68
##    es_paper  b_adj pval_b_adj ci_l_b_adj ci_u_b_adj tval_b_adj se_b_adj
##       <dbl>  <dbl>      <dbl>      <dbl>      <dbl>      <dbl>    <dbl>
##  1     0.27  0.12       0.016      0.022      0.22      2.440   0.04918
##  2     0.27  0.021      0.55      -0.05       0.092     0.5995  0.03503
##  3     0.36  0.13       0.012      0.026      0.23      2.551   0.05095
##  4     0.36  0.046      0.24      -0.025      0.12      1.182   0.03891
##  5     0.25  0.1        0.039     NA         NA         2.085   0.04796
##  6     0.25  0.038      0.28      -0.03       0.11      1.085   0.03501
##  7     0.48  0.17       0.001      0.026      0.23      3.375   0.05037
##  8     0.48  0.004      0.91      -0.025      0.12      0.1133  0.03529
##  9     0.03  0.064      0.471     -0.11       0.23      0.7231  0.08851
## 10     0.03  0.11       0.09      -0.016      0.24      1.710   0.06432
## 11     0.15  0.11       0.24      -0.071      0.29      1.181   0.09311
## 12     0.15  0.15       0.025      0.02       0.28      2.277   0.06586
## 13     0.27  0.23       0.01       0.059      0.4       2.614   0.08800
## 14     0.27  0.064      0.31      -0.062      0.19      1.020   0.06276
## 15     0.23  0.21       0.026      0.027      0.39      2.254   0.09315
## 16     0.23  0.14       0.042      0.006      0.27      2.061   0.06794
## 17    -0.04  0.066      0.85      -0.6        0.74      0.1895  0.3483 
## 18    -0.04 -0.65       0.008     -1.14      -0.16      2.697  -0.2410 
## 19     0.15 -0.34       0.35      -1.05       0.37      0.9382 -0.3624 
## 20     0.15 -0.49       0.07      -1.02       0.042     1.832  -0.2675
wide_format_Timmermans2012 <- function(data, filter){
  
  filter_func <- function(data, filter){
    
    dat <- data |> dplyr::filter(timing != filter)
    
    dat |> 
    dplyr::mutate(group = case_match(group, "Treatment" ~ "t", "Control" ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N_start:dplyr::last_col()
    )
    
    
  }
  
  purrr::pmap_dfr(list(filter), ~ filter_func(data = data, filter = .x )) |> 
    arrange(outcome)
  
  
}

# Continue from here

Timmermans2012_wide <- 
wide_format_Timmermans2012(data = Timmermans2012, filter = c("6 months", "3 months"))

rm(wide_format_Timmermans2012)

Effect sizes calculation

Timmermans2012_es <- 
  Timmermans2012 |>
  summarise(
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Timmermans et al. 2012",
    main_es_method = "Pretest adj. means from repeated-measure ANOVA",
    
    ppcor = .76,  
    ppcor_method = "From study (p. 57)",
    
    
    N_t = N[1],
    N_c = N[2],
    
    N_total = N_t + N_c, 
    
    df_ind = N_total,
    
    n_covariates = 1,
    
    sd_pool = sqrt( sum((N-1)*sd_post^2)/ (N_total-2) ),
    m_diff_post = m_post[1] - m_post[2],
    
    # Pretest adjusted means
    mean_b_adj = b_adj[1] - b_adj[2],
    
    # Difference in differences measures
    m_diff_t = m_post[1] - m_pre[1],  
    m_diff_c = m_post[2] - m_pre[2],
    
    DD = m_diff_t - m_diff_c,
    
    es_paper = es_paper[1],
    es_paper_type = "Cohens d",
    
    # Converting Loneliness measure since "Possible scores range from 0 to 11, 
    # with higher scores indicating more loneliness." (p. 58)
    d_post = if_else(unique(outcome) == "Loneliness", m_diff_post/sd_pool * -1, m_diff_post/sd_pool),
    vd_post = sum(1/N) + d_post^2/(2*df_ind),
    Wd_post = sum(1/N),
    
    d_adj = if_else(unique(outcome) == "Loneliness", mean_b_adj/sd_pool * -1, mean_b_adj/sd_pool),
    vd_adj = sum((se_b_adj/sd_pool)^2) + d_adj^2/(2*df_ind),
    Wd_adj = sum((se_b_adj/sd_pool)^2),
    
    
    d_DD = if_else(unique(outcome) == "Loneliness", DD/sd_pool * -1, DD/sd_pool),
    vd_DD = 2*(1-ppcor) * sum(1/N) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * sum(1/N),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = sum(1/N) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    g_adj = J * d_adj, 
    vg_adj = sum((se_b_adj/sd_pool)^2) + g_adj^2/(2*df_ind), 
    Wg_adj = Wd_adj,
    
    g_DD = J * d_DD,
    vg_DD = 2*(1-ppcor) * sum(1/N) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD,
    
    
    .by = c(outcome, timing) 
    
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # "was 7.0±2.1 (range of three to 12)" (p. 57)
    avg_cl_size = 7, 
    avg_cl_type = "From study (p. 57)",
    
    # Imputed icc value
    icc = 0.06,
    icc_type = "From study (p. 56)",
    
    n_covariates = 1,
  
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = vgt_post:Wgt_post
    ),
    
    gt_adj = omega * d_adj * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_adj, 
      model = "std_reg_coef",
      cluster_adj = TRUE,
      SE_std = sqrt(Wg_adj),
      add_name_to_vars = "_adj",
      vars = -var_term1_adj
    ),
    
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      )
    
  ) |> 
  ungroup()

# Timmermans2012_es

Timmermans2012_est <- 
  left_join(Timmermans2012_wide, Timmermans2012_es) |> 
  select(-c(es_paper_c, es_paper_t), -contains(c("ci_", "val_b"))) |> 
  mutate(vary_id = paste0(outcome, "/", timing),
         analysis_plan = case_when(
           outcome == "Empoverment"  ~  "Hope, Empowerment & Self-efficacy", 
           outcome == "Hope"  ~ "Hope, Empowerment & Self-efficacy", 
           outcome == "Quality"  ~ "Wellbeing and Quality of Life", 
           outcome == "Self-e"  ~ "All mental health outcomes", 
           outcome == "Loneliness"  ~ "Loneliness"
         )
      ) |> 
  rename(m_post = m_diff_post, mean_diff = DD)

Gatz et al. (2007) (Quality-checked)

Entering data from Table 2 (p. 872)

Gatz2007 <- tibble(
  outcome = rep(c("ASI alcohol", "ASI drug", "GSI", "PSS", "Coping skills"), each = 2),
  group = rep(c("Intervention", "Comparison"), dplyr::n_distinct(outcome)),
  N = c(136, 177, 135, 176, 136, 177, 136, 177, 134, 173),
  N_start = rep(c(187, 215), each = 5),
  
  m_pre = c(
    .18, .27, 
    .19, .25, 
    1.07, 1.09, 
    20.43, 19.05,
    52.61, 54.26),
  
  m_post = c(
    .06, .13, 
    .04, .08, 
    .8, .86, 
    13.78, 15.11,
    56.32, 52.96),
  
  sd_pre = c(
    .29, .35, 
    .15, .14, 
    .68, .69, 
    10.22, 11.84,
    17.83, 17.51),
  
  sd_post = c(
    .17, .23, 
    .07, .11, 
    .72, .77, 
    11.1, 12.91,
    20.15, 20.09)
)

Effect size calculation and cluster bias adjustment

Gatz2007_est <- 
  Gatz2007 |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Comparison" ~ "c")) |> 
  tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    ) |> 
  mutate(
    F_val = c(0.53, 1.23, 0.34, 3.99, 4.12), # Table 2 (p. 872)
    
    analysis_plan = c(
      "Alcohol and drug abuse/misuse", "Alcohol and drug abuse/misuse",
      "All mental health outcomes", "All mental health outcomes", "Hope, Empowerment & Self-efficacy"
    ),
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Gatz et al. 2012",
    main_es_method = "Raw diff-in-diffs",
    
    m_post = m_post_t - m_post_c,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # Account for the fact that some outcomes are on different scales
    m_diff_t = m_post_t - m_pre_t, 
    m_diff_c = m_post_c - m_pre_c, 
      
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = d_DD^2/F_val + d_DD^2/(2*df_ind),
    Wd_DD = d_DD^2/F_val,
    
    g_DD = J * d_DD, 
    vg_DD = g_DD^2/F_val + g_DD^2/(2*df_ind),
    Wg_DD = g_DD^2/F_val
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # Imputed since group sizes were not reported
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    ppcor_method = "F value used",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),

    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      F_val = F_val,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Gatz2007_est
## # A tibble: 5 × 61
##   outcome         N_t   N_c N_start_t N_start_c m_pre_t m_pre_c m_post_t
##   <chr>         <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>
## 1 ASI alcohol     136   177       187       187    0.18    0.27     0.06
## 2 ASI drug        135   176       187       187    0.19    0.25     0.04
## 3 GSI             136   177       187       215    1.07    1.09     0.8 
## 4 PSS             136   177       215       215   20.43   19.05    13.78
## 5 Coping skills   134   173       215       215   52.61   54.26    56.32
##   m_post_c sd_pre_t sd_pre_c sd_post_t sd_post_c F_val
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl> <dbl>
## 1     0.13     0.29     0.35      0.17      0.23  0.53
## 2     0.08     0.15     0.14      0.07      0.11  1.23
## 3     0.86     0.68     0.69      0.72      0.77  0.34
## 4    15.11    10.22    11.84     11.1      12.91  3.99
## 5    52.96    17.83    17.51     20.15     20.09  4.12
##   analysis_plan                     N_total df_ind effect_size
##   <chr>                               <dbl>  <dbl> <chr>      
## 1 Alcohol and drug abuse/misuse         313    313 SMD        
## 2 Alcohol and drug abuse/misuse         311    311 SMD        
## 3 All mental health outcomes            313    313 SMD        
## 4 All mental health outcomes            313    313 SMD        
## 5 Hope, Empowerment & Self-efficacy     307    307 SMD        
##   sd_used            study            main_es_method      m_post  sd_pool
##   <chr>              <chr>            <chr>                <dbl>    <dbl>
## 1 Pooled posttest SD Gatz et al. 2012 Raw diff-in-diffs -0.07     0.2061 
## 2 Pooled posttest SD Gatz et al. 2012 Raw diff-in-diffs -0.04     0.09475
## 3 Pooled posttest SD Gatz et al. 2012 Raw diff-in-diffs -0.06000  0.7487 
## 4 Pooled posttest SD Gatz et al. 2012 Raw diff-in-diffs -1.33    12.16   
## 5 Pooled posttest SD Gatz et al. 2012 Raw diff-in-diffs  3.36    20.12   
##     d_post vd_post Wd_post      J   g_post vg_post Wg_post m_diff_t m_diff_c
##      <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 -0.3396  0.01319 0.01300 0.9976 -0.3388  0.01319 0.01300    -0.12   -0.14 
## 2 -0.4222  0.01338 0.01309 0.9976 -0.4211  0.01337 0.01309    -0.15   -0.17 
## 3 -0.08014 0.01301 0.01300 0.9976 -0.07995 0.01301 0.01300    -0.27   -0.23 
## 4 -0.1094  0.01302 0.01300 0.9976 -0.1091  0.01302 0.01300    -6.65   -3.94 
## 5  0.1670  0.01329 0.01324 0.9976  0.1666  0.01329 0.01324     3.71   -1.300
##       d_DD    vd_DD    Wd_DD     g_DD    vg_DD    Wg_DD avg_cl_size avg_cl_type
##      <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>       <dbl> <chr>      
## 1  0.09703 0.01778  0.01777   0.09680 0.01770  0.01768            8 Imputed    
## 2  0.2111  0.03630  0.03622   0.2106  0.03612  0.03605            8 Imputed    
## 3 -0.05343 0.008400 0.008395 -0.05330 0.008359 0.008355           8 Imputed    
## 4 -0.2229  0.01253  0.01245  -0.2224  0.01247  0.01239            8 Imputed    
## 5  0.2491  0.01516  0.01506   0.2484  0.01508  0.01498            8 Imputed    
##     icc icc_type ppcor_method n_covariates gamma_sqrt df_adj  omega  gt_post
##   <dbl> <chr>    <chr>               <dbl>      <dbl>  <dbl>  <dbl>    <dbl>
## 1   0.1 Imputed  F value used            1       0.97  300.4 0.9975 -0.3286 
## 2   0.1 Imputed  F value used            1       0.97  298.5 0.9975 -0.4085 
## 3   0.1 Imputed  F value used            1       0.97  300.4 0.9975 -0.07754
## 4   0.1 Imputed  F value used            1       0.97  300.4 0.9975 -0.1059 
## 5   0.1 Imputed  F value used            1       0.97  294.6 0.9975  0.1616 
##   vgt_post Wgt_post    gt_DD  vgt_DD  Wgt_DD    hg_DD   vhg_DD  h_DD df_DD
##      <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>    <dbl> <dbl> <dbl>
## 1  0.01776  0.01758  0.09389 0.02250 0.02249  0.03612 0.003329 300.4 300.4
## 2  0.01799  0.01771  0.2042  0.04595 0.04588  0.05517 0.003350 298.5 298.5
## 3  0.01759  0.01758 -0.05169 0.01063 0.01063 -0.02893 0.003329 300.4 300.4
## 4  0.01760  0.01758 -0.2157  0.01584 0.01576 -0.09903 0.003329 300.4 300.4
## 5  0.01794  0.01789  0.2410  0.01914 0.01904  0.1017  0.003395 294.6 294.6
##   n_covariates_DD adj_fct_DD adj_value_DD vary_id      
##             <dbl> <chr>             <dbl> <chr>        
## 1               1 eta               1.352 ASI alcohol  
## 2               1 eta               1.353 ASI drug     
## 3               1 eta               1.352 GSI          
## 4               1 eta               1.352 PSS          
## 5               1 eta               1.351 Coping skills

Gonzalez and Prihoda (2007) (Quality-checked)

Data extracted from Table 3 (p. 413)

Gonzalez2007_est <- 
  tibble(
    study = "Gonzalez & Prihoda 2007",
    
    analysis_plan = c(
      "Social functioning (degree of impairment)",
      "All mental health outcomes"
    ),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD (assumed)",
    
    main_es_method = "Repeated-measure ANOVA with emm",
    ppcor_method = "F value used",
    
    outcome = c("GAF", "CGI"),
    N_start_t = 11, 
    N_start_c = 11,
    # Actual samples sizes
    N_t = 8, # Three patients terminated the group early (p. 411)
    N_c = 9, # Two of the control subjects were also lost two follow-up (p. 412)
    
    N_total = N_t + N_c,
    
    es_paper = c(0.20, 0.66),
    es_paper_type = "Cohen d (assumed)",
    
    b_emm_post_t = c(72.20, 1.75),
    b_emm_post_c = c(69.30, 2.61),
    
    se_emm_post_t = c(4.11, 0.37),
    se_emm_post_c = c(4.11, 0.37),
    
    mean_b_emm = if_else(outcome == "GAF", (b_emm_post_t - b_emm_post_c), (b_emm_post_t - b_emm_post_c) * -1),
    
    sd_total = abs(mean_b_emm)/es_paper,
    # Using E.14 from WWC Handbook (ver. 5). We imputed the covariate-adjusted mean difference to obtain this estimate
    sd_pool = sqrt( ((N_total-1)/(N_total-2)) * sd_total^2 - ((N_t*N_c)/(N_total*(N_total-2))) * mean_b_emm^2),
    
    pval_paper = c(
      0.65, 
      0.15 # Is reported as <0.05 and must be estimated from the corresponding F_val
      ),
    F_val = c(0.22, 2.34),
    
    #pval_test = pf(F_val, df1 = 1, df2 = 11, lower.tail = FALSE)

    df_ind = N_total,
    
    d_adj = es_paper,
    vd_adj = d_adj^2/F_val + d_adj^2/(2*N_total),
    Wd_adj = d_adj^2/F_val,
    
    J = 1 - 3/(4*df_ind-1),
    
    g_adj = J * d_adj,
    vg_adj = g_adj^2/F_val + g_adj^2/(2*N_total),
    Wg_adj = g_adj^2/F_val
  
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # he group, with 7 to 11 active members
    avg_cl_size = 7, 
    avg_cl_type = "From study (p. 408)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
  
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_adj = omega * d_adj * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_adj, 
      model = "emmeans",
      cluster_adj = FALSE,
      F_val = F_val,
      add_name_to_vars = "_adj",
      vars = -var_term1_adj
    ),
    
    gt_adj_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * (mean_b_emm/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_adj_pop = if_else(
      str_detect(outcome, "GAF"),
      # We did not use the F value for variance estimation here
      GAF_pop_res$WaboveT * (1/N_t + 1/N_c) * adj_value_adj + gt_adj_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_adj_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (1/N_t + 1/N_c) * adj_value_adj,
      NA_real_
      ),
    
    vary_id = outcome
    
  ); Gonzalez2007_est
## # A tibble: 2 × 52
## # Rowwise: 
##   study                   analysis_plan                             effect_size
##   <chr>                   <chr>                                     <chr>      
## 1 Gonzalez & Prihoda 2007 Social functioning (degree of impairment) SMD        
## 2 Gonzalez & Prihoda 2007 All mental health outcomes                SMD        
##   sd_used                      main_es_method                  ppcor_method
##   <chr>                        <chr>                           <chr>       
## 1 Pooled posttest SD (assumed) Repeated-measure ANOVA with emm F value used
## 2 Pooled posttest SD (assumed) Repeated-measure ANOVA with emm F value used
##   outcome N_start_t N_start_c   N_t   N_c N_total es_paper es_paper_type    
##   <chr>       <dbl>     <dbl> <dbl> <dbl>   <dbl>    <dbl> <chr>            
## 1 GAF            11        11     8     9      17     0.2  Cohen d (assumed)
## 2 CGI            11        11     8     9      17     0.66 Cohen d (assumed)
##   b_emm_post_t b_emm_post_c se_emm_post_t se_emm_post_c mean_b_emm sd_total
##          <dbl>        <dbl>         <dbl>         <dbl>      <dbl>    <dbl>
## 1        72.2         69.3           4.11          4.11      2.900   14.50 
## 2         1.75         2.61          0.37          0.37      0.86     1.303
##   sd_pool pval_paper F_val df_ind d_adj vd_adj Wd_adj      J  g_adj vg_adj
##     <dbl>      <dbl> <dbl>  <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1  14.90        0.65  0.22     17  0.2  0.1830 0.1818 0.9552 0.1910 0.1670
## 2   1.266       0.15  2.34     17  0.66 0.1990 0.1862 0.9552 0.6304 0.1815
##   Wg_adj avg_cl_size avg_cl_type           icc icc_type n_covariates gamma_sqrt
##    <dbl>       <dbl> <chr>               <dbl> <chr>           <dbl>      <dbl>
## 1 0.1659           7 From study (p. 408)   0.1 Imputed             1      0.952
## 2 0.1699           7 From study (p. 408)   0.1 Imputed             1      0.952
##   df_adj  omega gt_adj vgt_adj Wgt_adj hg_adj vhg_adj h_adj n_covariates_adj
##    <dbl>  <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl> <dbl>            <dbl>
## 1  14.92 0.9489 0.1807  0.1897  0.1886 0.1076 0.06702 14.92                1
## 2  14.92 0.9489 0.5962  0.2050  0.1931 0.3478 0.06702 14.92                1
##   adj_fct_adj adj_value_adj gt_adj_pop vgt_adj_pop Wgt_adj_pop vary_id
##   <chr>               <dbl>      <dbl>       <dbl>       <dbl> <chr>  
## 1 eta                 1.271     0.2057      0.2869      0.2868 GAF    
## 2 eta                 1.271    NA          NA          NA      CGI

Gordon et al. (2018) (Quality-checked)

Entering data from Table 2 (p. 124)

Gordon2018 <- tibble(
  
  outcome = as.factor(rep(c(
   "QLS_total",
   #"QLS_interpersonal",
   #"QLS_ instrumental",
   #"BLERT",
   "LSP",
   #"SCSQ-ToM",
   "SSPA",
   "DASS-21"
   #"AIHQ-Hostility-bias"
   ), 
   each = 2)),
  
  group = rep(c("SCIT", "Control"), n_distinct(outcome)),

  N = rep(c(21, 15), n_distinct(outcome)),
  N_start = rep(c(21, 15), 4),
 
 m_pre = c(
   50.24, 45.73, # QLS total
   #15.86, 13.40, # QLS interpersonal subscale 
   #6.19, 6.00,   # QLS instrumental subscale
   #14.43, 14.40, # BLERT
   13.38, 14.53, # LSP
   #7.33, 7.07,   # SCSQ-ToM
   65.19, 59.07, # SSPA
   43.33, 53.33  # DASS-21
    # 8.43, 8.53   # AIHQ-Hostility-bias
  ),  
 
 sd_pre = c(
   15.83, 13.85, 
   #7.26, 7.18, 
   #5.94, 6.21, 
   #3.62, 3.15, 
   7.44, 6.12, 
   #1.62, 1.79, 
   12.03, 10.57, 
   26.20, 32.73 
   #3.80, 4.24
 ),
 
 m_post = c(
   56.30, 47.67, 
   #17.40, 14.47, 
   #6.95, 6.53, 
   #15.90, 15.93, 
   10.29, 13.60,
   #7.50, 7.07, 
   71.25, 63.57, 
   41.10, 49.13
   #6.80, 8.87
 ),
 
 sd_post = c(
   16.34, 13.19, 
   #8.33, 6.57, 
   #5.48, 6.63, 
   #3.62, 2.76, 
   4.59, 6.10, 
   #1.53,1.40, 
   8.22, 7.89, 
   25.96, 31.40 
   #1.73, 2.29
 )
 
); Gordon2018
## # A tibble: 8 × 8
##   outcome   group       N N_start m_pre sd_pre m_post sd_post
##   <fct>     <chr>   <dbl>   <dbl> <dbl>  <dbl>  <dbl>   <dbl>
## 1 QLS_total SCIT       21      21 50.24  15.83  56.3    16.34
## 2 QLS_total Control    15      15 45.73  13.85  47.67   13.19
## 3 LSP       SCIT       21      21 13.38   7.44  10.29    4.59
## 4 LSP       Control    15      15 14.53   6.12  13.6     6.1 
## 5 SSPA      SCIT       21      21 65.19  12.03  71.25    8.22
## 6 SSPA      Control    15      15 59.07  10.57  63.57    7.89
## 7 DASS-21   SCIT       21      21 43.33  26.2   41.1    25.96
## 8 DASS-21   Control    15      15 53.33  32.73  49.13   31.4

Effect size calculation and cluster bias adjustment

Gordon2018_est <- 
  Gordon2018 |> 
  mutate(group = case_match(group, "SCIT" ~ "t", "Control" ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    ) |> 
  mutate(
    F_val = c(
      2.503, #0.019, 
      0.905, #0.632, 
      0.039, 0.573 # 2.99
      ), 
    
    analysis_plan = case_when(
      outcome == "DASS-21" ~ "All mental health outcomes",
      str_detect(outcome, "SSPA|LSP") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "QLS") ~ "Wellbeing and Quality of Life",
      TRUE ~ NA_character_
    ),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Gordon et al. 2018",
    main_es_method = "Raw diff-in-diffs",
    
    
    ppcor = ppcor_imp,  
    ppcor_method = "Imputed - F-vals seemed flawed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Calculate d post
    m_post = m_post_t - m_post_c,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # Account for the fact that some outcomes are on different scales
    m_diff_t = m_post_t - m_pre_t, 
    m_diff_c = m_post_c - m_pre_c, 
      
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # Imputed since group sizes were not reported
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),

    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Gordon2018_est
## # A tibble: 4 × 62
##   outcome     N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t sd_pre_c
##   <fct>     <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 QLS_total    21    15        21        15   50.24   45.73    15.83    13.85
## 2 LSP          21    15        21        15   13.38   14.53     7.44     6.12
## 3 SSPA         21    15        21        15   65.19   59.07    12.03    10.57
## 4 DASS-21      21    15        21        15   43.33   53.33    26.2     32.73
##   m_post_t m_post_c sd_post_t sd_post_c F_val
##      <dbl>    <dbl>     <dbl>     <dbl> <dbl>
## 1    56.3     47.67     16.34     13.19 2.503
## 2    10.29    13.6       4.59      6.1  0.905
## 3    71.25    63.57      8.22      7.89 0.039
## 4    41.1     49.13     25.96     31.4  0.573
##   analysis_plan                             effect_size sd_used           
##   <chr>                                     <chr>       <chr>             
## 1 Wellbeing and Quality of Life             SMD         Pooled posttest SD
## 2 Social functioning (degree of impairment) SMD         Pooled posttest SD
## 3 Social functioning (degree of impairment) SMD         Pooled posttest SD
## 4 All mental health outcomes                SMD         Pooled posttest SD
##   study              main_es_method    ppcor ppcor_method                  
##   <chr>              <chr>             <dbl> <chr>                         
## 1 Gordon et al. 2018 Raw diff-in-diffs   0.5 Imputed - F-vals seemed flawed
## 2 Gordon et al. 2018 Raw diff-in-diffs   0.5 Imputed - F-vals seemed flawed
## 3 Gordon et al. 2018 Raw diff-in-diffs   0.5 Imputed - F-vals seemed flawed
## 4 Gordon et al. 2018 Raw diff-in-diffs   0.5 Imputed - F-vals seemed flawed
##   N_total df_ind m_post sd_pool  d_post vd_post Wd_post      J  g_post vg_post
##     <dbl>  <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>
## 1      36     36   8.63  15.12   0.5707  0.1188  0.1143 0.9790  0.5587  0.1186
## 2      36     36  -3.31   5.264 -0.6287  0.1198  0.1143 0.9790 -0.6156  0.1195
## 3      36     36   7.68   8.086  0.9498  0.1268  0.1143 0.9790  0.9299  0.1263
## 4      36     36  -8.03  28.33  -0.2835  0.1154  0.1143 0.9790 -0.2775  0.1154
##   Wg_post m_diff_t m_diff_c     d_DD  vd_DD  Wd_DD     g_DD  vg_DD  Wg_DD
##     <dbl>    <dbl>    <dbl>    <dbl>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl>
## 1  0.1143    6.060    1.940  0.2724  0.1153 0.1143  0.2667  0.1153 0.1143
## 2  0.1143   -3.090   -0.93  -0.4103  0.1166 0.1143 -0.4017  0.1165 0.1143
## 3  0.1143    6.06     4.5    0.1929  0.1148 0.1143  0.1889  0.1148 0.1143
## 4  0.1143   -2.230   -4.200  0.06955 0.1144 0.1143  0.06809 0.1144 0.1143
##   avg_cl_size avg_cl_type   icc icc_type n_covariates gamma_sqrt df_adj  omega
##         <dbl> <chr>       <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
## 1           8 Imputed       0.1 Imputed             1      0.969  32.91 0.9770
## 2           8 Imputed       0.1 Imputed             1      0.969  32.91 0.9770
## 3           8 Imputed       0.1 Imputed             1      0.969  32.91 0.9770
## 4           8 Imputed       0.1 Imputed             1      0.969  32.91 0.9770
##   gt_post vgt_post Wgt_post    gt_DD vgt_DD Wgt_DD    hg_DD  vhg_DD  h_DD df_DD
##     <dbl>    <dbl>    <dbl>    <dbl>  <dbl>  <dbl>    <dbl>   <dbl> <dbl> <dbl>
## 1  0.5403   0.1453   0.1409  0.2579  0.1419 0.1409  0.1196  0.03039 32.91 32.91
## 2 -0.5953   0.1463   0.1409 -0.3884  0.1432 0.1409 -0.1799  0.03039 32.91 32.91
## 3  0.8992   0.1532   0.1409  0.1827  0.1414 0.1409  0.08477 0.03039 32.91 32.91
## 4 -0.2684   0.1420   0.1409  0.06584 0.1410 0.1409  0.03057 0.03039 32.91 32.91
##   n_covariates_DD adj_fct_DD adj_value_DD vary_id  
##             <dbl> <chr>             <dbl> <fct>    
## 1               1 eta               1.233 QLS_total
## 2               1 eta               1.233 LSP      
## 3               1 eta               1.233 SSPA     
## 4               1 eta               1.233 DASS-21

Gutman et al. (2019) (Quality-checked)

Entering raw data from Table 2 (p. 67)

Gutman2019_raw <- 
  tibble(
    outcome = rep(c("PSS", "WHOQOL-BREF"), each = 20),
    
    group = rep(c("t", "c"), each = 10, 2),
    
    N = 10,
    
    pre_test = c(
      25, 37, 34, 33, 19, 25, 36, 34, 32, 35, # PSS intervention
      25, 30, 31, 34, 26, 21, 32, 34, 27, 35, # PSS Control
      121, 114, 84, 89, 82, 83, 86, 70, 119, 93, # WHOQOL intervention
      97, 113, 98, 93, 86, 93, 98, 57, 91, 101 # WHOQOL control
    ),
    
    post_test = c(
      21, 32, 30, 29, 15, 23, 32, 27, 28, 30, 
      27, 30, 33, 41, 30, 27, 32, 36, 33, 36,
      125, 118, 95, 89, 87, 91, 93, 77, 125, 107,
      100, 112, 95, 90, 88, 85, 100, 43, 74, 102
    )
    
  ) |> 
  mutate(
    across(pre_test:post_test, ~ (.x - mean(.x))/sd(.x), .names = "{.col}_std"),
    
    .by = outcome
    
  )

Effect size calculation

# Calculate raw means first and combine with _est object
Gutman2019 <- 
  Gutman2019_raw |> 
  summarise(
    
    N = length(post_test),
    
    m_pre = mean(pre_test),
    sd_pre = sd(pre_test),
    m_post = mean(post_test),
    sd_post = sd(post_test),
    
    r = cor(pre_test, post_test),
    
    .by = c(outcome, group)
  ) |> 
  pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  ) |> 
  mutate(
    N_total = N_t + N_c,
    N_start_t = 21, # p. 120
    N_start_c = 15,
    df_ind = N_total
  )

Gutman2019_cor <- 
  Gutman2019_raw |> 
  summarise(
    
    ppcor = cor(pre_test, post_test, method = "pearson"),
    ppcor_method = "From study - raw data",
    
    .by = outcome
  )

Gutman2019 <- left_join(Gutman2019, Gutman2019_cor, by = join_by(outcome))

Gutman2019_est <-
  Gutman2019_raw |> 
  summarise(
    
    d_reg = summary(lm(post_test_std ~ group + pre_test_std, data = pick(everything())))$coefficients[2],
    var_term1_reg = summary(lm(post_test_std ~ group + pre_test_std, data = pick(everything())))$coefficients[5]^2,
    
    .by = outcome
  
  ) |> 
  mutate(
    
    d_reg = if_else(outcome == "PSS", d_reg * -1, d_reg)
    
  ) |> 
  left_join(Gutman2019, by = join_by(outcome)) |> 
  mutate(
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Gutman et al. 2019",
    main_es_method = "Standardized regression from reported raw data",
    
    ppcor = Gutman2019_cor$ppcor,
    ppcor_method = Gutman2019_cor$ppcor_method,
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Calculate d post
    m_post = if_else(outcome == "WHOQOL-BREF", m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c), 
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post
    
    
  ) |> 
  relocate(c(d_reg:var_term1_reg), .after = last_col()) |> 
  mutate(
    
  vd_reg = var_term1_reg + d_reg^2/(2*df_ind),
  Wd_reg = var_term1_reg,
  
  g_reg = J * d_reg, 
  vg_reg = var_term1_reg + g_reg^2/(2*df_ind),
  Wg_reg = var_term1_reg,
  
  ) |>
  rowwise() |> 
  mutate(
  avg_cl_size = 10, # Assuming one cluster of 10 only 
  avg_cl_type = "From study",
    
  # Imputed icc value
  icc = ICC_01,
  icc_type = "Imputed",
  
  n_covariates = 1, 
  
  gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
 
  # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
  df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
  omega = 1 - 3/(4*df_adj-1),
    
  gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
  
  gt_reg = omega * d_reg * gamma_sqrt,
  VIVECampbell::vgt_smd_1armcluster(
    N_cl_grp = N_t,
    N_ind_grp = N_c,
    avg_grp_size = avg_cl_size,
    ICC = icc, 
    g = gt_reg, 
    model = "std_reg_coef",
    cluster_adj = FALSE,
    SE_std = var_term1_reg,
    add_name_to_vars = "_reg"
  ),
  
  vary_id = outcome,

  analysis_plan = case_when(
      str_detect(outcome, "PSS") ~ "All mental health outcomes",
      str_detect(outcome, "WHO") ~ "Wellbeing and Quality of Life"),
    
  ) |> 
  select(-var_term1_reg); Gutman2019_est
## # A tibble: 2 × 61
## # Rowwise: 
##   outcome       N_t   N_c m_pre_t m_pre_c sd_pre_t sd_pre_c m_post_t m_post_c
##   <chr>       <int> <int>   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
## 1 PSS            10    10    31      29.5    5.925    4.601     26.7     32.5
## 2 WHOQOL-BREF    10    10    94.1    92.7   17.59    14.45     100.7     88.9
##   sd_post_t sd_post_c    r_t    r_c N_total N_start_t N_start_c df_ind  ppcor
##       <dbl>     <dbl>  <dbl>  <dbl>   <int>     <dbl>     <dbl>  <int>  <dbl>
## 1     5.458     4.353 0.9792 0.8351      20        21        15     20 0.6994
## 2    16.97     19.25  0.9753 0.9533      20        21        15     20 0.9147
##   ppcor_method          effect_size sd_used            study             
##   <chr>                 <chr>       <chr>              <chr>             
## 1 From study - raw data SMD         Pooled posttest SD Gutman et al. 2019
## 2 From study - raw data SMD         Pooled posttest SD Gutman et al. 2019
##   main_es_method                                 m_post sd_pool d_post vd_post
##   <chr>                                           <dbl>   <dbl>  <dbl>   <dbl>
## 1 Standardized regression from reported raw data    5.8   4.936 1.175   0.2345
## 2 Standardized regression from reported raw data   11.8  18.15  0.6503  0.2106
##   Wd_post      J g_post vg_post Wg_post  d_reg  vd_reg  Wd_reg  g_reg  vg_reg
##     <dbl>  <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl>
## 1     0.2 0.9620 1.130   0.2319     0.2 1.255  0.06350 0.02416 1.207  0.06057
## 2     0.2 0.9620 0.6256  0.2098     0.2 0.5515 0.02630 0.01869 0.5306 0.02573
##    Wg_reg avg_cl_size avg_cl_type   icc icc_type n_covariates gamma_sqrt df_adj
##     <dbl>       <dbl> <chr>       <dbl> <chr>           <dbl>      <dbl>  <dbl>
## 1 0.02416          10 From study    0.1 Imputed             1      0.949  17.81
## 2 0.01869          10 From study    0.1 Imputed             1      0.949  17.81
##    omega gt_post vgt_post Wgt_post gt_reg  vgt_reg   Wgt_reg hg_reg vhg_reg
##    <dbl>   <dbl>    <dbl>    <dbl>  <dbl>    <dbl>     <dbl>  <dbl>   <dbl>
## 1 0.9573  1.067    0.3117     0.28 1.140  0.03690  0.0008169  3.667 0.05556
## 2 0.9573  0.5908   0.2897     0.28 0.5010 0.007462 0.0004893  2.883 0.05556
##   h_reg df_reg n_covariates_reg adj_fct_reg adj_value_reg vary_id    
##   <dbl>  <dbl>            <dbl> <chr>               <dbl> <chr>      
## 1    18     18                1 eta                   1.4 PSS        
## 2    18     18                1 eta                   1.4 WHOQOL-BREF
##   analysis_plan                
##   <chr>                        
## 1 All mental health outcomes   
## 2 Wellbeing and Quality of Life

Hagen et al. (2005) (Quality-checked)

Entering data from Table 2 (p. 39)

# Data from table 2, p. 39 
Hagen2005 <- tibble(
  
  outcome = rep(c(
    
  "SCL-90",
  "BAI",
  "BDI",
  "IIP-64-C"
  #"SAS-A",
  #"SAS-S",
  #"YSQ"
  ), 
  each = 2,1
 ),
 
 group = rep(c(
     
   "waiting-list", "CBGT"
   ),
   each = 1,4
 ),
 
 N = c(
     17, 15, 
     17, 15, 
     15, 15, 
     17, 14 
     # 16, 14, 
     # 16, 15, 
     # 16, 14
   ),
 
 N_start = rep(c(17,15), each = 1,4),
 
 m_pre = c(
     1.19, 1.15,
     20.29, 22.60,
     18.20, 15.40,
     1.35, 1.22
    # 73.06, 62.93,
    #  73.87, 70.80, 
    #  11.50, 7.21
 ),
 
  sd_pre = c(
      0.61, 0.61,
      10.08, 14.77,
      8.55, 9.86,
      0.72, 0.63
     # 20.23, 15.66,
     # 20.26, 19.16,
     # 14.15, 8.62
  ) ,
 
 m_post = c(
   1.15, 0.88, 
   22.94, 18.13, 
   18.35, 11.00, 
   1.28, 1.18 
  # 60.12, 58.57, 
  # 69.12, 60.14, 
  # 10.17, 6.63
 ),
 
 sd_post = c(
   0.47, 0.59,  
   13.46, 12.00,
   9.00, 8.36,
   0.68, 0.76
  # 14.17, 18.67,
  # 13.99, 22.52,
  # 13.41, 11.16
 ),

); Hagen2005
## # A tibble: 8 × 8
##   outcome  group            N N_start m_pre sd_pre m_post sd_post
##   <chr>    <chr>        <dbl>   <dbl> <dbl>  <dbl>  <dbl>   <dbl>
## 1 SCL-90   waiting-list    17      17  1.19   0.61   1.15    0.47
## 2 SCL-90   CBGT            15      15  1.15   0.61   0.88    0.59
## 3 BAI      waiting-list    17      17 20.29  10.08  22.94   13.46
## 4 BAI      CBGT            15      15 22.6   14.77  18.13   12   
## 5 BDI      waiting-list    15      17 18.2    8.55  18.35    9   
## 6 BDI      CBGT            15      15 15.4    9.86  11       8.36
## 7 IIP-64-C waiting-list    17      17  1.35   0.72   1.28    0.68
## 8 IIP-64-C CBGT            14      15  1.22   0.63   1.18    0.76
# Making the Hagen2006 tibble wide in order to estimate the effect sizes and
# further analysis. 

Hagen2005_wide <-
  Hagen2005 |> 
  mutate (group = case_match(
    group, "CBGT"  ~ "t", 
    "waiting-list" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )

# Effect size calculating 
Hagen2005_est <-           
  Hagen2005_wide |>
  mutate(
    
    analysis_plan = case_when(
      str_detect(outcome, "SCL-90") ~ "All mental health outcomes",
      str_detect(outcome, "BAI") ~ "All mental health outcomes/Anxiety",
      str_detect(outcome, "BDI") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "IIP-64-C") ~ "Social functioning (degree of impairment)",
      .default = NA_character_
    ),
    
    # ANCOVA results from table 3 (p. 39)
  #  F_val = c(
    #  8.46,
  #    2.23,
   #   5.51,
  #    0.18
  #  ),
    
  #  df1 = c(1),
  #  df2= c(
  #    28,
  #    28,
  #    27,
  #    27
  #  )
  
  ) |> 
  mutate(
    # Imputed ppcor
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    study = "Hagen et al. 2005",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # For all measures lower scores are beneficial why these are reverted
    m_post = (m_post_t - m_post_c)*-1, 
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # All outcomes are reverted because lower score is beneficial
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |>
  rowwise() |> 
   mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # Average cluster size in treatment group
    # Harp intevention had six to ten partcipants 
    
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 35)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD),
    
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
    )|> 
  ungroup() |> 
  mutate(vary_id = outcome); Hagen2005_est
## # A tibble: 4 × 64
##   outcome    N_c   N_t N_start_c N_start_t m_pre_c m_pre_t sd_pre_c sd_pre_t
##   <chr>    <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 SCL-90      17    15        17        15    1.19    1.15     0.61     0.61
## 2 BAI         17    15        17        15   20.29   22.6     10.08    14.77
## 3 BDI         15    15        17        15   18.2    15.4      8.55     9.86
## 4 IIP-64-C    17    14        17        15    1.35    1.22     0.72     0.63
##   m_post_c m_post_t sd_post_c sd_post_t
##      <dbl>    <dbl>     <dbl>     <dbl>
## 1     1.15     0.88      0.47      0.59
## 2    22.94    18.13     13.46     12   
## 3    18.35    11         9         8.36
## 4     1.28     1.18      0.68      0.76
##   analysis_plan                             ppcor ppcor_method study            
##   <chr>                                     <dbl> <chr>        <chr>            
## 1 All mental health outcomes                  0.5 Imputed      Hagen et al. 2005
## 2 All mental health outcomes/Anxiety          0.5 Imputed      Hagen et al. 2005
## 3 All mental health outcomes/Depression       0.5 Imputed      Hagen et al. 2005
## 4 Social functioning (degree of impairment)   0.5 Imputed      Hagen et al. 2005
##   effect_size sd_used             main_es_method    N_total df_ind m_post
##   <chr>       <chr>               <chr>               <dbl>  <dbl>  <dbl>
## 1 SMD         Pooled posttest SDs Raw diff-in-diffs      32     32 0.27  
## 2 SMD         Pooled posttest SDs Raw diff-in-diffs      32     32 4.81  
## 3 SMD         Pooled posttest SDs Raw diff-in-diffs      30     30 7.35  
## 4 SMD         Pooled posttest SDs Raw diff-in-diffs      31     31 0.1000
##   sd_pool d_post vd_post Wd_post      J g_post vg_post Wg_post m_diff_t m_diff_c
##     <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1  0.5294 0.5100  0.1296  0.1255 0.9764 0.4980  0.1294  0.1255  0.27     0.04000
## 2 12.80   0.3758  0.1277  0.1255 0.9764 0.3669  0.1276  0.1255  4.47    -2.650  
## 3  8.686  0.8462  0.1453  0.1333 0.9748 0.8249  0.1447  0.1333  4.4     -0.1500 
## 4  0.7170 0.1395  0.1306  0.1303 0.9756 0.1361  0.1306  0.1303  0.04000  0.07000
##       d_DD  vd_DD  Wd_DD     g_DD  vg_DD  Wg_DD avg_cl_size avg_cl_type       
##      <dbl>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl>       <dbl> <chr>             
## 1  0.4345  0.1284 0.1255  0.4242  0.1283 0.1255           8 From study (p. 35)
## 2  0.5563  0.1303 0.1255  0.5431  0.1301 0.1255           8 From study (p. 35)
## 3  0.5238  0.1379 0.1333  0.5106  0.1377 0.1333           8 From study (p. 35)
## 4 -0.04184 0.1303 0.1303 -0.04082 0.1303 0.1303           8 From study (p. 35)
##     icc icc_type n_covariates gamma_sqrt df_adj  omega gt_post vgt_post Wgt_post
##   <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>   <dbl>    <dbl>    <dbl>
## 1   0.1 Imputed             1      0.961  29.38 0.9743  0.4775   0.1702   0.1663
## 2   0.1 Imputed             1      0.961  29.38 0.9743  0.3518   0.1684   0.1663
## 3   0.1 Imputed             1      0.962  27.38 0.9724  0.7915   0.1848   0.1733
## 4   0.1 Imputed             1      0.96   28.46 0.9734  0.1303   0.1747   0.1744
##      gt_DD vgt_DD Wgt_DD    hg_DD  vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD
##      <dbl>  <dbl>  <dbl>    <dbl>   <dbl> <dbl> <dbl>           <dbl> <chr>     
## 1  0.4068  0.1691 0.1663  0.1835  0.03404 29.38 29.38               1 eta       
## 2  0.5208  0.1709 0.1663  0.2346  0.03404 29.38 29.38               1 eta       
## 3  0.4900  0.1777 0.1733  0.2240  0.03652 27.38 27.38               1 eta       
## 4 -0.03910 0.1744 0.1744 -0.01755 0.03514 28.46 28.46               1 eta       
##   adj_value_DD gt_DD_pop vgt_DD_pop Wgt_DD_pop vary_id 
##          <dbl>     <dbl>      <dbl>      <dbl> <chr>   
## 1        1.325   NA         NA         NA      SCL-90  
## 2        1.325   NA         NA         NA      BAI     
## 3        1.3      0.3553     0.1686     0.1684 BDI     
## 4        1.339   NA         NA         NA      IIP-64-C

Haslam et al. (2019) (Quality-checked)

Entering data from Table 2 (p. 794)

# Study only provides raw means and standard errors for pre-measurements and only estimated marginal 
# means with standard errors for post-measurements.

#NOT USED
Haslam2019 <- tibble(
  group = rep(c("Group 4 health",
                "TAU"), each = 1,5
  ),
  outcome =rep(c(
    "Loneliness",
    "Depression",
    "Social Anxiety",
    "General practitioner vists",
    "Multiple group memberships"
  ), each = 2,1),
  
  N = rep(c(66,54), each = 1,5), 
  
  # Table 1, p. 791
  m_pre = c(
    25.13, 25.00,
    20.82, 19.70,
    3.39, 3.53,
    1.41, 0.78,
    1.75, 1.84
  ),
  
  sd_pre = c(
    3.11, 2.78,
    9.96, 9.40,
    1.11, 1.05,
    1.65, 0.97,
    0.86, 0.84
  ),
  
# Table 3, p. 796
  emm_post = c(
    20.40, 23.51,
    17.66, 19.80,
    3.02, 3.57,
    2.19, 2.32,
    2.65, 2.16
  ),
  
  se_post = c(
    0.42, 0.47,
    1.19, 1.33,
    0.08, 0.09,
    0.27, 0.31,
    0.10, 0.11
  )

); Haslam2019
## # A tibble: 10 × 7
##    group          outcome                        N m_pre sd_pre emm_post se_post
##    <chr>          <chr>                      <dbl> <dbl>  <dbl>    <dbl>   <dbl>
##  1 Group 4 health Loneliness                    66 25.13   3.11    20.4     0.42
##  2 TAU            Loneliness                    54 25      2.78    23.51    0.47
##  3 Group 4 health Depression                    66 20.82   9.96    17.66    1.19
##  4 TAU            Depression                    54 19.7    9.4     19.8     1.33
##  5 Group 4 health Social Anxiety                66  3.39   1.11     3.02    0.08
##  6 TAU            Social Anxiety                54  3.53   1.05     3.57    0.09
##  7 Group 4 health General practitioner vists    66  1.41   1.65     2.19    0.27
##  8 TAU            General practitioner vists    54  0.78   0.97     2.32    0.31
##  9 Group 4 health Multiple group memberships    66  1.75   0.86     2.65    0.1 
## 10 TAU            Multiple group memberships    54  1.84   0.84     2.16    0.11
# coefficients extracted from table 2, p. 794 
# USED FOR META-ANALYSIS
Haslam2019_est <- tibble(
  
  study = "Haslem 2019",
  
  effect_size = "SMD",
  sd_used = "Pooled pre-test SD - only SDs reported",
  
  main_es_method = "Mixed-effects/multi-level model reg coef",
  
  outcome = c(
   "Loneliness",
   "Depression",
   "Social Anxiety",
   "General practitioner vists",
   "Multiple group memberships"
  ),
  
  analysis_plan = case_when(
      str_detect(outcome, "Loneliness") ~ "Loneliness",
      str_detect(outcome, "Depression") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "Social Anxiety") ~ "All mental health outcomes",
      str_detect(outcome, "General practitioner vists|Multiple group memberships") ~ "Unused outcomes",
      .default = NA_character_
    ),
  
  N_start_t = 66,
  N_start_c = 54,
    
  N_t = N_start_t, 
  N_c = N_start_c, 
  N_total = N_t + N_c, 

  df_ind = N_total,
  
  # I have taken the beta values for the Time X Condition
  b_reg = c(
    -0.743,
    -0.334,
    -0.525,
    -0.752,
     0.523
  ),
  
  se_b_reg = c(
    0.218,
    0.206,
    0.169,
    0.255, 
    0.197
  ),
  
  sd_pre_t = c(3.11, 9.96, 1.11, 1.65, 0.86),
  sd_pre_c = c(2.78, 9.40, 1.05, 0.97, 0.84),
  
  sd_pool = sqrt(((N_t-1)*sd_pre_t^2 + (N_c-1)*sd_pre_c^2)/(N_t + N_c - 2)),
  
  d_reg = b_reg/sd_pool,
  vd_reg = (se_b_reg/sd_pool)^2 + d_reg^2/(2*N_total),
  Wd_reg = (se_b_reg/sd_pool)^2,
  
  J = 1 - 3/(4*df_ind-1),
  
  g_reg = J * d_reg, 
  vg_reg = (se_b_reg/sd_pool)^2 + g_reg^2/(2*N_total),
  Wg_reg = (se_b_reg/sd_pool)^2,
  
  # icc (group-level) from study
  icc = c(.054, .084, .034, .081, .056),
  icc_type = "From study (Table 2, p. 794)",
  
 ) |> 
  rowwise() |> 
  mutate(

  #"In the present study, the program was administered in groups comprising between five and nine participants" (p. 792)
  avg_cl_size = 5, 
  avg_cl_type = "From study (p. 792)",
  
  
  n_covariates = 1,

  # Find info about the function via ?VIVECampbell::gamma_1armcluster
  gamma_sqrt = VIVECampbell::gamma_1armcluster(
    N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
  ),
  
  # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
  df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
  omega = 1 - 3/(4*df_adj-1),
  
  
  gt_reg = omega * d_reg * gamma_sqrt,
  VIVECampbell::vgt_smd_1armcluster(
    N_cl_grp = N_t,
    N_ind_grp = N_c,
    avg_grp_size = avg_cl_size,
    ICC = icc, 
    g = gt_reg, 
    model = "reg_coef",
    cluster_adj = TRUE,
    SE = se_b_reg,
    SD = sd_pool,
    add_name_to_vars = "_reg",
    vars = -var_term1_reg
  ),
  
  vary_id = outcome
  ) |> 
  ungroup(); Haslam2019_est 
## # A tibble: 5 × 43
##   study       effect_size sd_used                               
##   <chr>       <chr>       <chr>                                 
## 1 Haslem 2019 SMD         Pooled pre-test SD - only SDs reported
## 2 Haslem 2019 SMD         Pooled pre-test SD - only SDs reported
## 3 Haslem 2019 SMD         Pooled pre-test SD - only SDs reported
## 4 Haslem 2019 SMD         Pooled pre-test SD - only SDs reported
## 5 Haslem 2019 SMD         Pooled pre-test SD - only SDs reported
##   main_es_method                           outcome                   
##   <chr>                                    <chr>                     
## 1 Mixed-effects/multi-level model reg coef Loneliness                
## 2 Mixed-effects/multi-level model reg coef Depression                
## 3 Mixed-effects/multi-level model reg coef Social Anxiety            
## 4 Mixed-effects/multi-level model reg coef General practitioner vists
## 5 Mixed-effects/multi-level model reg coef Multiple group memberships
##   analysis_plan                         N_start_t N_start_c   N_t   N_c N_total
##   <chr>                                     <dbl>     <dbl> <dbl> <dbl>   <dbl>
## 1 Loneliness                                   66        54    66    54     120
## 2 All mental health outcomes/Depression        66        54    66    54     120
## 3 All mental health outcomes                   66        54    66    54     120
## 4 Unused outcomes                              66        54    66    54     120
## 5 Unused outcomes                              66        54    66    54     120
##   df_ind  b_reg se_b_reg sd_pre_t sd_pre_c sd_pool    d_reg    vd_reg    Wd_reg
##    <dbl>  <dbl>    <dbl>    <dbl>    <dbl>   <dbl>    <dbl>     <dbl>     <dbl>
## 1    120 -0.743    0.218     3.11     2.78  2.966  -0.2505  0.005662  0.005401 
## 2    120 -0.334    0.206     9.96     9.4   9.712  -0.03439 0.0004548 0.0004499
## 3    120 -0.525    0.169     1.11     1.05  1.083  -0.4846  0.02531   0.02433  
## 4    120 -0.752    0.255     1.65     0.97  1.386  -0.5424  0.03505   0.03383  
## 5    120  0.523    0.197     0.86     0.84  0.8511  0.6145  0.05515   0.05358  
##        J    g_reg    vg_reg    Wg_reg   icc icc_type                    
##    <dbl>    <dbl>     <dbl>     <dbl> <dbl> <chr>                       
## 1 0.9937 -0.2489  0.005659  0.005401  0.054 From study (Table 2, p. 794)
## 2 0.9937 -0.03417 0.0004547 0.0004499 0.084 From study (Table 2, p. 794)
## 3 0.9937 -0.4815  0.02530   0.02433   0.034 From study (Table 2, p. 794)
## 4 0.9937 -0.5390  0.03504   0.03383   0.081 From study (Table 2, p. 794)
## 5 0.9937  0.6107  0.05513   0.05358   0.056 From study (Table 2, p. 794)
##   avg_cl_size avg_cl_type         n_covariates gamma_sqrt df_adj  omega   gt_reg
##         <dbl> <chr>                      <dbl>      <dbl>  <dbl>  <dbl>    <dbl>
## 1           5 From study (p. 792)            1      0.987  117.2 0.9936 -0.2456 
## 2           5 From study (p. 792)            1      0.98   115.9 0.9935 -0.03348
## 3           5 From study (p. 792)            1      0.992  117.7 0.9936 -0.4776 
## 4           5 From study (p. 792)            1      0.98   116.1 0.9935 -0.5281 
## 5           5 From study (p. 792)            1      0.986  117.1 0.9936  0.6020 
##     vgt_reg   Wgt_reg  hg_reg  vhg_reg h_reg df_reg n_covariates_reg adj_fct_reg
##       <dbl>     <dbl>   <dbl>    <dbl> <dbl>  <dbl>            <dbl> <chr>      
## 1 0.005518  0.005261  -0.3104 0.008535 117.2  117.2                1 gamma      
## 2 0.0004362 0.0004314 -0.1494 0.008625 115.9  115.9                1 gamma      
## 3 0.02491   0.02394   -0.2827 0.008498 117.7  117.7                1 gamma      
## 4 0.03371   0.03251   -0.2702 0.008614 116.1  116.1                1 gamma      
## 5 0.05368   0.05213    0.2425 0.008540 117.1  117.1                1 gamma      
##   adj_value_reg vary_id                   
##           <dbl> <chr>                     
## 1         0.974 Loneliness                
## 2         0.959 Depression                
## 3         0.984 Social Anxiety            
## 4         0.961 General practitioner vists
## 5         0.973 Multiple group memberships

Hilden et al. (2021) (Quality-checked)

Entering data from Table 2 (p. 181)

# Table 2, p. 181
Hilden2021 <- tibble(
  outcome = rep(c(
    "BSL-23 total",   
    "Overall Anxiety Severity and Impairment scale",  
    "Patient Health Questionnaire 9 scale",    
    "Alcohol Use Disorders Identification Test scale",
    "Sheehan disability work or studylife",
    "Sheehan disability social life",
    "Sheehan disability family life"
  ), each = 2, 1), 
  
  
  group = rep(c(
    "Schema therapy group",
    "TAU"
  ), each = 1, 7), 
  
  N = rep(c(23,12), each = 1, 7), 
  N_start = rep(c(25,12), each = 1, 7),
  
  m_pre = c(
    39., 55.7, 
    11.3, 13.2, 
    14, 16.3,
    6.4, 8.4,
    6., 6.8,
    5.2, 6.7,
    5.2, 6.7),
  
  sd_pre = c(
    15.1, 14.9, 
    3.8, 2.6, 
    5.7, 4.1,
    4.8, 5.6,
    3.1, 2.3,
    2.8, 1.6,
    2.8, 1.6),
  
  m_post = c(
    32., 42.6, 
    10.3, 11.4, 
    5.7, 9.2,
    11.9, 14.3,
    5.2, 6.9,
    5.6, 5.9,
    4.9, 6.3),
  
  sd_post = c(
    16.4, 18.8, 
    3.9, 3.5, 
    5.5, 8.2,
    5.2, 5.9,
    3.3, 2.4,
    2.6, 2.7,
    2.9, 2.1)
  
)



# Making the tibble into wide format
Hilden2021_wide <-
  Hilden2021 |> 
  mutate(group = case_when(
    group == "Schema therapy group" ~ "t", 
    group == "TAU" ~ "c",
    TRUE ~ NA_character_
  )) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )

Hilden2021_est <-           
  Hilden2021_wide |>
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "BSL-23 total") ~ "All mental health outcomes",
      str_detect(outcome, "Overall Anxiety") ~ "All mental health outcomes/Anxiety",
      str_detect(outcome, "Patient Health") ~ "Physical health",
      str_detect(outcome, "Alcohol Use Disorders") ~ "Alcohol and drug abuse/misuse",
      str_detect(outcome, "Sheehan disability") ~ "Social functioning (degree of impairment)",
      TRUE ~ NA_character_
    ),
    
    study = "Hilden et al. 2021",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor is imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # For Social functioning lower scores is beneficial, hence these are reverted
    m_post = if_else(outcome %in% c("BSL-23 total", 
                                    "Patient Health Questionnaire 9 scale",
                                    "Overall Anxiety Severity and Impairment scale",
                                    "Alcohol Use Disorders Identification Test scale"), 
                     (m_post_t - m_post_c) * -1,
                     m_post_t - m_post_c), 
    
    sd_pool = sqrt(((N_t - 1) * sd_post_t^2 + (N_c - 1) * sd_post_c^2) / (N_t + N_c - 2)),  
    
    d_post = m_post / sd_pool, 
    vd_post = (1 / N_t + 1 / N_c) + d_post^2 / (2 * df_ind),
    Wd_post = (1 / N_t + 1 / N_c),
    
    J = 1 - 3 / (4 * df_ind - 1),
    
    g_post = J * d_post,
    vg_post = (1 / N_t + 1 / N_c) + g_post^2 / (2 * df_ind),
    Wg_post = Wd_post,
    
    # For Social functioning lower scores is beneficial, hence these are reverted
    m_diff_t = if_else(outcome %in% c("BSL-23 total", 
                                      "Patient Health Questionnaire 9 scale",
                                      "Overall Anxiety Severity and Impairment scale",
                                      "Alcohol Use Disorders Identification Test scale"),
                       (m_post_t - m_pre_t) * -1,
                       m_post_t - m_pre_t),
    
    m_diff_c = if_else(outcome %in% c("BSL-23 total", 
                                      "Patient Health Questionnaire 9 scale",
                                      "Overall Anxiety Severity and Impairment scale",
                                      "Alcohol Use Disorders Identification Test scale"),
                       (m_post_c - m_pre_c) * -1,
                       m_post_c - m_pre_c), 
    
    d_DD = (m_diff_t - m_diff_c) / sd_pool,
    vd_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + d_DD^2 / (2 * df_ind),
    Wd_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + g_DD^2 / (2 * df_ind),
    Wg_DD = Wd_DD
  ) |> 
  rowwise() |>
  mutate(
    
    # Average cluster size in treatment group
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 177)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3 / (4 * df_adj - 1),
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c("vgt_post", "Wgt_post")
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -c("var_term1_DD")
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "9"),
      omega * ((m_diff_t - m_diff_c)/PHQ_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*PHQ_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
  ) |> 
  ungroup() |> 
  mutate(
    vary_id = outcome
  )

Hilden2021_est
## # A tibble: 7 × 64
##   outcome                                           N_t   N_c N_start_t
##   <chr>                                           <dbl> <dbl>     <dbl>
## 1 BSL-23 total                                       23    12        25
## 2 Overall Anxiety Severity and Impairment scale      23    12        25
## 3 Patient Health Questionnaire 9 scale               23    12        25
## 4 Alcohol Use Disorders Identification Test scale    23    12        25
## 5 Sheehan disability work or studylife               23    12        25
## 6 Sheehan disability social life                     23    12        25
## 7 Sheehan disability family life                     23    12        25
##   N_start_c m_pre_t m_pre_c sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t
##       <dbl>   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>     <dbl>
## 1        12    39      55.7     15.1     14.9     32       42.6      16.4
## 2        12    11.3    13.2      3.8      2.6     10.3     11.4       3.9
## 3        12    14      16.3      5.7      4.1      5.7      9.2       5.5
## 4        12     6.4     8.4      4.8      5.6     11.9     14.3       5.2
## 5        12     6       6.8      3.1      2.3      5.2      6.9       3.3
## 6        12     5.2     6.7      2.8      1.6      5.6      5.9       2.6
## 7        12     5.2     6.7      2.8      1.6      4.9      6.3       2.9
##   sd_post_c analysis_plan                             study             
##       <dbl> <chr>                                     <chr>             
## 1      18.8 All mental health outcomes                Hilden et al. 2021
## 2       3.5 All mental health outcomes/Anxiety        Hilden et al. 2021
## 3       8.2 Physical health                           Hilden et al. 2021
## 4       5.9 Alcohol and drug abuse/misuse             Hilden et al. 2021
## 5       2.4 Social functioning (degree of impairment) Hilden et al. 2021
## 6       2.7 Social functioning (degree of impairment) Hilden et al. 2021
## 7       2.1 Social functioning (degree of impairment) Hilden et al. 2021
##   effect_size sd_used             main_es_method    ppcor ppcor_method N_total
##   <chr>       <chr>               <chr>             <dbl> <chr>          <dbl>
## 1 SMD         Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           35
## 2 SMD         Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           35
## 3 SMD         Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           35
## 4 SMD         Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           35
## 5 SMD         Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           35
## 6 SMD         Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           35
## 7 SMD         Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           35
##   df_ind  m_post sd_pool  d_post vd_post Wd_post      J  g_post vg_post Wg_post
##    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>
## 1     35 10.6     17.24   0.6150  0.1322  0.1268 0.9784  0.6017  0.1320  0.1268
## 2     35  1.1      3.771  0.2917  0.1280  0.1268 0.9784  0.2854  0.1280  0.1268
## 3     35  3.5      6.525  0.5364  0.1309  0.1268 0.9784  0.5248  0.1307  0.1268
## 4     35  2.4      5.443  0.4409  0.1296  0.1268 0.9784  0.4314  0.1295  0.1268
## 5     35 -1.7      3.030 -0.5611  0.1313  0.1268 0.9784 -0.5490  0.1311  0.1268
## 6     35 -0.3000   2.634 -0.1139  0.1270  0.1268 0.9784 -0.1114  0.1270  0.1268
## 7     35 -1.4      2.660 -0.5263  0.1308  0.1268 0.9784 -0.5149  0.1306  0.1268
##   m_diff_t m_diff_c     d_DD  vd_DD  Wd_DD     g_DD  vg_DD  Wg_DD avg_cl_size
##      <dbl>    <dbl>    <dbl>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl>       <dbl>
## 1   7       13.1    -0.3539  0.1286 0.1268 -0.3462  0.1285 0.1268           6
## 2   1        1.800  -0.2121  0.1275 0.1268 -0.2075  0.1274 0.1268           6
## 3   8.3      7.1     0.1839  0.1273 0.1268  0.1799  0.1273 0.1268           6
## 4  -5.5     -5.9     0.07348 0.1269 0.1268  0.07190 0.1269 0.1268           6
## 5  -0.8      0.1000 -0.2970  0.1281 0.1268 -0.2906  0.1280 0.1268           6
## 6   0.4000  -0.8     0.4556  0.1298 0.1268  0.4458  0.1297 0.1268           6
## 7  -0.3000  -0.4000  0.03759 0.1268 0.1268  0.03678 0.1268 0.1268           6
##   avg_cl_type           icc icc_type n_covariates gamma_sqrt df_adj  omega
##   <chr>               <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
## 1 From study (p. 177)   0.1 Imputed             1      0.975     32 0.9764
## 2 From study (p. 177)   0.1 Imputed             1      0.975     32 0.9764
## 3 From study (p. 177)   0.1 Imputed             1      0.975     32 0.9764
## 4 From study (p. 177)   0.1 Imputed             1      0.975     32 0.9764
## 5 From study (p. 177)   0.1 Imputed             1      0.975     32 0.9764
## 6 From study (p. 177)   0.1 Imputed             1      0.975     32 0.9764
## 7 From study (p. 177)   0.1 Imputed             1      0.975     32 0.9764
##   gt_post vgt_post Wgt_post    gt_DD vgt_DD Wgt_DD    hg_DD  vhg_DD  h_DD df_DD
##     <dbl>    <dbl>    <dbl>    <dbl>  <dbl>  <dbl>    <dbl>   <dbl> <dbl> <dbl>
## 1  0.5854   0.1456   0.1403 -0.3369  0.1420 0.1403 -0.1587  0.03125    32    32
## 2  0.2777   0.1415   0.1403 -0.2019  0.1409 0.1403 -0.09525 0.03125    32    32
## 3  0.5106   0.1443   0.1403  0.1751  0.1407 0.1403  0.08259 0.03125    32    32
## 4  0.4197   0.1430   0.1403  0.06995 0.1403 0.1403  0.03302 0.03125    32    32
## 5 -0.5341   0.1447   0.1403 -0.2828  0.1415 0.1403 -0.1333  0.03125    32    32
## 6 -0.1084   0.1404   0.1403  0.4337  0.1432 0.1403  0.2040  0.03125    32    32
## 7 -0.5010   0.1442   0.1403  0.03579 0.1403 0.1403  0.01689 0.03125    32    32
##   n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop Wgt_DD_pop
##             <dbl> <chr>             <dbl>     <dbl>      <dbl>      <dbl>
## 1               1 eta               1.106   NA         NA         NA     
## 2               1 eta               1.106   NA         NA         NA     
## 3               1 eta               1.106    0.1886     0.1384     0.1382
## 4               1 eta               1.106   NA         NA         NA     
## 5               1 eta               1.106   NA         NA         NA     
## 6               1 eta               1.106   NA         NA         NA     
## 7               1 eta               1.106   NA         NA         NA     
##   vary_id                                        
##   <chr>                                          
## 1 BSL-23 total                                   
## 2 Overall Anxiety Severity and Impairment scale  
## 3 Patient Health Questionnaire 9 scale           
## 4 Alcohol Use Disorders Identification Test scale
## 5 Sheehan disability work or studylife           
## 6 Sheehan disability social life                 
## 7 Sheehan disability family life

Himle et al. (2014) (Quality-checked)

Entering data from Table 2 (p. 174)

# From table 2, p. 174
Himle2014 <- 
  tibble(
    group = as.factor(rep(c("Intervention group",
                            "Control group"),
                          each = 1, 12)), 
    
    timing = rep(c("Post","3m"), each = 12,1), 
    
    
    outcome = rep(c(
      "LSAS -Total", 
      #"Work-related social anxiety- total", 
      "Brief fear of negative evaluation", 
      "Mini social phobia inventory", 
      "Beck anxiety inventory (BAI)", 
      "PHQ9 depression screen", 
      "Sheehan disability scale"
      #  "Social phobia symptom severity", 
      #  "Clinician global impressions - symptom severity" 
    ), each = 2, 2), 
    
    N = 29,
    
    N_start = 29,
    
    m_pre = rep(c(
      84.31, 87.59, 
      #  18.72, 19.72,
      46.59, 45.,
      6.72, 7.52,
      17.69, 22.9,
      10.59, 12.17,
      5.93, 6.07
      #  2.07, 2.1,
      # 4.59, 4.24)
    ),
    each = 1,2),
    
    sd_pre = rep(c(
      31.51, 26.33, 
      #   7.42, 6.53,
      12.83, 13.19,
      2.2, 2.87,
      12.83, 14.73,
      8.08, 5.62,
      2.6, 2.33
      #  .65, .62,
      #  1.52, .99)
    ),
    each = 1,2),
    
    m_post = c(
      
      #Postvalues
      66.72, 90.62, 
      #  13.21, 19.76,
      39.01, 43.07,
      7.79, 9.66,
      11.34, 24.72,
      5.62, 10.41,
      3.63, 4.77,
      #  1.31, 2.17,
      #  3.76, 4.14,
      
      #3m follow-up
      65.31, 92.79, 
      # 13.31, 19.31,
      35.62, 44.,
      7.48, 10.28,
      12.83, 22.45,
      5.83, 10.72,
      3.48, 5.61
      #  1.28, 2.21,
      #  3.21, 4.21
    ),
    
    ## Standard deviation-values 
    sd_post = c(
      # Post SD values
      29.02, 36.67, 
      # 7.10, 7.83,
      13.57, 14.48,
      2.81, 2.58,
      9.54, 17.10,
      5.49, 6.92,
      2.1, 2.02,
      #  .81, .66,
      #  1.35, 1.27,
      
      37.44, 36.26, 
      #  8.38, 7.72,
      13.57, 12.57,
      3.01, 1.28,
      13.4, 14.96,
      5.74, 6.68,
      2.27, 2.38
      #    .84, .73,
      #    1.35, 1.37
    )
  )

Himle2014_wide <- 
  Himle2014|> 
  mutate(group = case_match(group, "Intervention group" ~ "t", "Control group" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  ) |> 
  relocate(outcome)

# Multi level effect estimates can be obtained from table 3, p. 3
Himle2014_est <- 
  Himle2014_wide |> 
  mutate(
    # analysis_plan = rep(c(
    #  "the Liebowitz Social Anxiety Scale",
    # "The Brief Fear of Negative Evaluation scale",
    #  "The Mini Social Phobia Inventory",
    #  "Beck Anxiety Inventory",
    #  "Patient Health Questionnaire (PHQ-9)",
    #  "Shehan disability scale"
    # ), each = 2),  # assuming there are two rows per each analysis_plan, adjust if necessary
    
    analysis_plan = case_when(
      str_detect(outcome, "LSAS|Mini|Brief") ~ "All mental health outcomes",
      str_detect(outcome, "Beck") ~ "All mental health outcomes/Anxiety",
      str_detect(outcome, "Sheehan") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "PHQ9") ~ "Physical health",
      TRUE ~ NA_character_
    ),
    
    study = "Himle et al. 2014"
    
  ) |> 
  mutate(
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor is imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # MHV: ER VI HELT SIKRE PÅ DETTE?
    # Reverting all outcomes 
    m_post = (m_post_t - m_post_c) * -1,
    
    sd_pool = sqrt(((N_t - 1) * sd_post_t^2 + (N_c - 1) * sd_post_c^2) / (N_t + N_c - 2)),  
    
    d_post = m_post / sd_pool, 
    vd_post = (1 / N_t + 1 / N_c) + d_post^2 / (2 * df_ind),
    Wd_post = (1 / N_t + 1 / N_c),
    
    J = 1 - 3 / (4 * df_ind - 1),
    
    g_post = J * d_post,
    vg_post = (1 / N_t + 1 / N_c) + g_post^2 / (2 * df_ind),
    Wg_post = Wd_post,
    
    # Reverting all outcomes
    m_diff_t = (m_post_t - m_pre_t) * -1,
    m_diff_c = (m_post_c - m_pre_c) * -1,
    
    d_DD = (m_diff_t - m_diff_c) / sd_pool,
    vd_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + d_DD^2 / (2 * df_ind),
    Wd_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + g_DD^2 / (2 * df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 170)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3 / (4 * df_adj - 1),
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c("vgt_post", "Wgt_post")
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -c("var_term1_DD")
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "9"),
      omega * ((m_diff_t - m_diff_c)/PHQ_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*PHQ_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
  ) |> 
  ungroup() |> 
  mutate(
    vary_id = paste0(outcome, "/", timing)
  )

Himle2014_est
## # A tibble: 12 × 65
##    outcome                           timing   N_t   N_c N_start_t N_start_c
##    <chr>                             <chr>  <dbl> <dbl>     <dbl>     <dbl>
##  1 LSAS -Total                       Post      29    29        29        29
##  2 Brief fear of negative evaluation Post      29    29        29        29
##  3 Mini social phobia inventory      Post      29    29        29        29
##  4 Beck anxiety inventory (BAI)      Post      29    29        29        29
##  5 PHQ9 depression screen            Post      29    29        29        29
##  6 Sheehan disability scale          Post      29    29        29        29
##  7 LSAS -Total                       3m        29    29        29        29
##  8 Brief fear of negative evaluation 3m        29    29        29        29
##  9 Mini social phobia inventory      3m        29    29        29        29
## 10 Beck anxiety inventory (BAI)      3m        29    29        29        29
## 11 PHQ9 depression screen            3m        29    29        29        29
## 12 Sheehan disability scale          3m        29    29        29        29
##    m_pre_t m_pre_c sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##      <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
##  1   84.31   87.59    31.51    26.33    66.72    90.62     29.02     36.67
##  2   46.59   45       12.83    13.19    39.01    43.07     13.57     14.48
##  3    6.72    7.52     2.2      2.87     7.79     9.66      2.81      2.58
##  4   17.69   22.9     12.83    14.73    11.34    24.72      9.54     17.1 
##  5   10.59   12.17     8.08     5.62     5.62    10.41      5.49      6.92
##  6    5.93    6.07     2.6      2.33     3.63     4.77      2.1       2.02
##  7   84.31   87.59    31.51    26.33    65.31    92.79     37.44     36.26
##  8   46.59   45       12.83    13.19    35.62    44        13.57     12.57
##  9    6.72    7.52     2.2      2.87     7.48    10.28      3.01      1.28
## 10   17.69   22.9     12.83    14.73    12.83    22.45     13.4      14.96
## 11   10.59   12.17     8.08     5.62     5.83    10.72      5.74      6.68
## 12    5.93    6.07     2.6      2.33     3.48     5.61      2.27      2.38
##    analysis_plan                             study             effect_size
##    <chr>                                     <chr>             <chr>      
##  1 All mental health outcomes                Himle et al. 2014 SMD        
##  2 All mental health outcomes                Himle et al. 2014 SMD        
##  3 All mental health outcomes                Himle et al. 2014 SMD        
##  4 All mental health outcomes/Anxiety        Himle et al. 2014 SMD        
##  5 Physical health                           Himle et al. 2014 SMD        
##  6 Social functioning (degree of impairment) Himle et al. 2014 SMD        
##  7 All mental health outcomes                Himle et al. 2014 SMD        
##  8 All mental health outcomes                Himle et al. 2014 SMD        
##  9 All mental health outcomes                Himle et al. 2014 SMD        
## 10 All mental health outcomes/Anxiety        Himle et al. 2014 SMD        
## 11 Physical health                           Himle et al. 2014 SMD        
## 12 Social functioning (degree of impairment) Himle et al. 2014 SMD        
##    sd_used             main_es_method    ppcor ppcor_method N_total df_ind
##    <chr>               <chr>             <dbl> <chr>          <dbl>  <dbl>
##  1 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
##  2 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
##  3 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
##  4 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
##  5 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
##  6 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
##  7 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
##  8 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
##  9 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
## 10 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
## 11 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
## 12 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           58     58
##    m_post sd_pool d_post vd_post Wd_post      J g_post vg_post Wg_post m_diff_t
##     <dbl>   <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
##  1  23.9   33.07  0.7228 0.07347 0.06897 0.9870 0.7134 0.07335 0.06897  17.59  
##  2   4.06  14.03  0.2893 0.06969 0.06897 0.9870 0.2856 0.06967 0.06897   7.580 
##  3   1.87   2.697 0.6932 0.07311 0.06897 0.9870 0.6842 0.07300 0.06897  -1.07  
##  4  13.38  13.85  0.9663 0.07702 0.06897 0.9870 0.9538 0.07681 0.06897   6.35  
##  5   4.79   6.246 0.7669 0.07404 0.06897 0.9870 0.7569 0.07390 0.06897   4.97  
##  6   1.14   2.060 0.5533 0.07160 0.06897 0.9870 0.5461 0.07154 0.06897   2.3   
##  7  27.48  36.85  0.7456 0.07376 0.06897 0.9870 0.7359 0.07363 0.06897  19     
##  8   8.38  13.08  0.6407 0.07250 0.06897 0.9870 0.6324 0.07241 0.06897  10.97  
##  9   2.8    2.313 1.211  0.08160 0.06897 0.9870 1.195  0.08127 0.06897  -0.7600
## 10   9.62  14.20  0.6774 0.07292 0.06897 0.9870 0.6686 0.07282 0.06897   4.86  
## 11   4.89   6.228 0.7852 0.07428 0.06897 0.9870 0.7750 0.07414 0.06897   4.76  
## 12   2.13   2.326 0.9159 0.07620 0.06897 0.9870 0.9040 0.07601 0.06897   2.45  
##    m_diff_c   d_DD   vd_DD   Wd_DD   g_DD   vg_DD   Wg_DD avg_cl_size
##       <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>       <dbl>
##  1  -3.03   0.6236 0.07232 0.06897 0.6155 0.07223 0.06897           6
##  2   1.93   0.4026 0.07036 0.06897 0.3974 0.07033 0.06897           6
##  3  -2.14   0.3967 0.07032 0.06897 0.3915 0.07029 0.06897           6
##  4  -1.82   0.5901 0.07197 0.06897 0.5824 0.07189 0.06897           6
##  5   1.76   0.5139 0.07124 0.06897 0.5072 0.07118 0.06897           6
##  6   1.3    0.4853 0.07100 0.06897 0.4790 0.07094 0.06897           6
##  7  -5.2    0.6566 0.07268 0.06897 0.6481 0.07259 0.06897           6
##  8   1      0.7623 0.07397 0.06897 0.7524 0.07385 0.06897           6
##  9  -2.76   0.8647 0.07541 0.06897 0.8535 0.07525 0.06897           6
## 10   0.4500 0.3105 0.06980 0.06897 0.3065 0.06978 0.06897           6
## 11   1.45   0.5315 0.07140 0.06897 0.5246 0.07134 0.06897           6
## 12   0.46   0.8557 0.07528 0.06897 0.8446 0.07511 0.06897           6
##    avg_cl_type           icc icc_type n_covariates gamma_sqrt df_adj  omega
##    <chr>               <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
##  1 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
##  2 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
##  3 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
##  4 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
##  5 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
##  6 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
##  7 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
##  8 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
##  9 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
## 10 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
## 11 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
## 12 From study (p. 170)   0.1 Imputed             1       0.97  54.59 0.9862
##    gt_post vgt_post Wgt_post  gt_DD  vgt_DD  Wgt_DD  hg_DD  vhg_DD  h_DD df_DD
##      <dbl>    <dbl>    <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl> <dbl> <dbl>
##  1  0.6914  0.08714  0.08276 0.5965 0.08602 0.08276 0.2788 0.01832 54.59 54.59
##  2  0.2768  0.08346  0.08276 0.3852 0.08412 0.08276 0.1807 0.01832 54.59 54.59
##  3  0.6632  0.08679  0.08276 0.3795 0.08408 0.08276 0.1781 0.01832 54.59 54.59
##  4  0.9244  0.09059  0.08276 0.5645 0.08568 0.08276 0.2640 0.01832 54.59 54.59
##  5  0.7336  0.08769  0.08276 0.4916 0.08497 0.08276 0.2303 0.01832 54.59 54.59
##  6  0.5293  0.08532  0.08276 0.4643 0.08473 0.08276 0.2176 0.01832 54.59 54.59
##  7  0.7133  0.08742  0.08276 0.6281 0.08637 0.08276 0.2934 0.01832 54.59 54.59
##  8  0.6129  0.08620  0.08276 0.7292 0.08763 0.08276 0.3398 0.01832 54.59 54.59
##  9  1.158   0.09504  0.08276 0.8272 0.08903 0.08276 0.3844 0.01832 54.59 54.59
## 10  0.6480  0.08660  0.08276 0.2971 0.08357 0.08276 0.1395 0.01832 54.59 54.59
## 11  0.7511  0.08793  0.08276 0.5084 0.08513 0.08276 0.2381 0.01832 54.59 54.59
## 12  0.8761  0.08979  0.08276 0.8185 0.08890 0.08276 0.3805 0.01832 54.59 54.59
##    n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop Wgt_DD_pop
##              <dbl> <chr>             <dbl>     <dbl>      <dbl>      <dbl>
##  1               1 eta                 1.2   NA        NA         NA      
##  2               1 eta                 1.2   NA        NA         NA      
##  3               1 eta                 1.2   NA        NA         NA      
##  4               1 eta                 1.2   NA        NA         NA      
##  5               1 eta                 1.2    0.5070    0.08326    0.08155
##  6               1 eta                 1.2   NA        NA         NA      
##  7               1 eta                 1.2   NA        NA         NA      
##  8               1 eta                 1.2   NA        NA         NA      
##  9               1 eta                 1.2   NA        NA         NA      
## 10               1 eta                 1.2   NA        NA         NA      
## 11               1 eta                 1.2    0.5228    0.08336    0.08155
## 12               1 eta                 1.2   NA        NA         NA      
##    vary_id                               
##    <chr>                                 
##  1 LSAS -Total/Post                      
##  2 Brief fear of negative evaluation/Post
##  3 Mini social phobia inventory/Post     
##  4 Beck anxiety inventory (BAI)/Post     
##  5 PHQ9 depression screen/Post           
##  6 Sheehan disability scale/Post         
##  7 LSAS -Total/3m                        
##  8 Brief fear of negative evaluation/3m  
##  9 Mini social phobia inventory/3m       
## 10 Beck anxiety inventory (BAI)/3m       
## 11 PHQ9 depression screen/3m             
## 12 Sheehan disability scale/3m

Izquierdo et al. (2021) (Excluded because flawed result reproted. See calculations below)

Entering data from Table 3 (p. 10) and Table 4 (p. 13)

# Rows 1-10 is data from Table 3, p. 10 and rows 11-22 is from Table 4 p. 13. 

izquierdo2021 <- tibble(
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,12)),
  timing = rep(c(
    "Post",
    "3m",
    "6m"
  ),
  each = 8,1),
  
  
  outcome = rep(c(
                  #"EE",
                  #"OE",
                  "CS", 
                  "ACI",
                  "SCI",
                  #"POSIT",
                  #"NEGAT",
                  #"AFFECT",
                  #"DISOR",
                  #"DISORG",
                  "Total_score"),
                each = 2,3), 
  
  N = rep(c(7)),
  
  
  m_pre = rep(c(
    # 6.57, 10.57, # EE
    # 6.86, 8.29,  # OE
    13.43, 18.86,# CS
    NA, NA,      # ACI - Basline data not reported
    NA, NA,      # SCI - Basline data not reported
    # 21.29, 15.14,# POSIT
    # 10.57, 6.86, # NEGAT
    # 35.86, 37.14,# AFFECT
    # 5.00, 3.86,  # DISOR
    # 4.86, 2.43,  # DISORG
    77.57, 65.43 # Total_score
    ), each = 1,3),
  
  sd_pre_reported = rep(c(
    # 2.44, 3.36,
    # 2.19, 1.38,
    2.44, 4.34,
    NA, NA,
    NA, NA,
    # 11.09, 5.84,
    # 4.24, 2.54,
    # 5.64, 4.56,
    # 3.00, 3.18, 
    # 2.67, 1.90,
    18.30, 10.85), 
    each = 1,3),
  
  sd_pre = sd_pre_reported *sqrt(7),
  
  m_post = c(
    
    # post measurement
    # 17.86, 10.57,
    # 16.29, 8.14,
    34.14, 18.71,
    36.57, 11.14,
    4.57, 1.14,
    # 8.14, 15.71,
    # 5.43, 7.71,
    # 16.86, 37.00,
    # 2.43, 3.86,
    # 1.29, 1.57,
    34.14, 65.86,
    
    # 3 months measurement
    #  16.00, 9.71,
    #  16.29, 7.57,
    32.29, 17.29,
    37.57, 11.00,
    4.57, 1.00,
    #  7.14, 15.71,
    #  5.00, 8.00,
    #  13.29, 35.14,
    #  2.57, 3.71,
    #  1.00, 1.14,
    29.00, 63.71,
    
    
    # 6 months measurement
    # 16.71, 9.71,
    # 15.71, 6.86,
    32.43, 16.57,
   37.14, 11.57,
    4.29, 1.14,
    #  7.29, 15.29,
    #  5.00, 8.29,
    #  13.00, 36.14,
    #  2.29, 3.57,
    #  1.00, 1.29,
    28.57, 64.57
    
  ),
  
  sd_post_reported = c(
  
  # post measurement
  #  1.86, 3.15,
  #  3.09, 1.35,
    4.81, 2.87,
    5.32, 3.19,
    0.54, 0.38,
  #  2.12, 6.40,
  #  1.27, 3.45,
  #  2.27, 3.74,
  #  0.53, 2.54,
  #  0.49, 0.79,
    3.13, 11.13,
  
  # 3 months measurement 
  #  3.21, 2.98,
  #  2.63, 2.07,
  5.28, 2.87,
  4.20, 3.21,
  0.53, 0.00,
  #  1.07, 6.97,
  #  0.82, 3.56,
  #  0.95, 4.70,
  #  0.79, 2.63,
  #  0.00, 0.38,
  1.83, 11.97,
  
  # 6 months measurement 
  # 16.71, 9.71,
  # 15.71, 6.86,
  5.19, 2.88,
  4.88, 2.94,
  0.76, 0.38,
  #  7.29, 15.29,
  #  5.00, 8.29,
  #  13.00, 36.14,
  #  2.29, 3.57,
  #  1.00, 1.29,
  1.81, 9.68
  ),
  
  sd_post = sd_post_reported * sqrt(7)
  

); izquierdo2021
## # A tibble: 24 × 10
##    group        timing outcome         N m_pre sd_pre_reported sd_pre m_post
##    <fct>        <chr>  <chr>       <dbl> <dbl>           <dbl>  <dbl>  <dbl>
##  1 Intervention Post   CS              7 13.43            2.44  6.456  34.14
##  2 Control      Post   CS              7 18.86            4.34 11.48   18.71
##  3 Intervention Post   ACI             7 NA              NA    NA      36.57
##  4 Control      Post   ACI             7 NA              NA    NA      11.14
##  5 Intervention Post   SCI             7 NA              NA    NA       4.57
##  6 Control      Post   SCI             7 NA              NA    NA       1.14
##  7 Intervention Post   Total_score     7 77.57           18.3  48.42   34.14
##  8 Control      Post   Total_score     7 65.43           10.85 28.71   65.86
##  9 Intervention 3m     CS              7 13.43            2.44  6.456  32.29
## 10 Control      3m     CS              7 18.86            4.34 11.48   17.29
## 11 Intervention 3m     ACI             7 NA              NA    NA      37.57
## 12 Control      3m     ACI             7 NA              NA    NA      11   
## 13 Intervention 3m     SCI             7 NA              NA    NA       4.57
## 14 Control      3m     SCI             7 NA              NA    NA       1   
## 15 Intervention 3m     Total_score     7 77.57           18.3  48.42   29   
## 16 Control      3m     Total_score     7 65.43           10.85 28.71   63.71
## 17 Intervention 6m     CS              7 13.43            2.44  6.456  32.43
## 18 Control      6m     CS              7 18.86            4.34 11.48   16.57
## 19 Intervention 6m     ACI             7 NA              NA    NA      37.14
## 20 Control      6m     ACI             7 NA              NA    NA      11.57
## 21 Intervention 6m     SCI             7 NA              NA    NA       4.29
## 22 Control      6m     SCI             7 NA              NA    NA       1.14
## 23 Intervention 6m     Total_score     7 77.57           18.3  48.42   28.57
## 24 Control      6m     Total_score     7 65.43           10.85 28.71   64.57
##    sd_post_reported sd_post
##               <dbl>   <dbl>
##  1             4.81  12.73 
##  2             2.87   7.593
##  3             5.32  14.08 
##  4             3.19   8.440
##  5             0.54   1.429
##  6             0.38   1.005
##  7             3.13   8.281
##  8            11.13  29.45 
##  9             5.28  13.97 
## 10             2.87   7.593
## 11             4.2   11.11 
## 12             3.21   8.493
## 13             0.53   1.402
## 14             0      0    
## 15             1.83   4.842
## 16            11.97  31.67 
## 17             5.19  13.73 
## 18             2.88   7.620
## 19             4.88  12.91 
## 20             2.94   7.779
## 21             0.76   2.011
## 22             0.38   1.005
## 23             1.81   4.789
## 24             9.68  25.61
# Turning data into wide format
params <- tibble(
  filter_val1 = rep(c("Post", paste0(c(3, 6), "m")), 1)
  
)



wide_izquierdo2021_func <- 
  function(filter_val1){
    
    izquierdo2021 |> 
      filter(timing == filter_val1) |> 
      mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
      )   
    
  }



izquierdo2021_est <- 
  pmap(params, wide_izquierdo2021_func) |>
  list_rbind() |> 
  mutate(
    analysis_plan = rep(
      c("Hope, Empowerment & Self-efficacy",
        "Social functioning (degree of impairment)",
        "Social functioning (degree of impairment)",
        "All mental health outcomes" # Total score
      ), each = 1,3),
    
    study = "Izquierdo et al. 2021",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Reverting m_post only for Total_score
    m_post = if_else(outcome == "Total_score", (m_post_t - m_post_c) * -1, m_post_t - m_post_c), 
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    #sd_pool = sqrt(((N_t-1)*sd_post_reported_t^2 + (N_c-1)*sd_post_reported_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    vary_id = paste0(outcome, "/", timing)
    
  ) |> 
  ungroup()

Jacob et al. (2010) (Quality-checked)

Entering data from Table 2 (p. 383)

Jacob2010 <- tibble(
  
  group = as.factor(rep(c("Intervention", "Control"), each = 1, 10)), 
  
  timing = rep(c("Post","6m"), each = 10,1),
  
  outcome = rep(c(
    "RSES",   
    "SSES Achievement",  
    "SSES Social",    
    "SSES Appearance",
    "BDI"), each = 2, 2), 
  

  N = rep(c(19, 24), each = 1,10),
  N_start = rep(c(19, 24), each = 1,10),

  m_pre = rep(c(
    13.3, 13.3, # Social Cognition Screening Questionnaire
    18.7, 19.2, # Brief Assessment of Cognition in Schizophrenia
    13.8, 16.3, # Positive and Negative Syndrome Scale
    11.7, 10.8, # Global Assessment of Functioning
    27.8, 30.0) # Social Functioning Scale
    , each = 1,2),
  
  
  sd_pre = rep(c(
    8.6, 9.9,
    4., 6.6,
    6.6, 7.4,
    5.7, 5.2,
    11.8, 12.2)
    , each = 1,2),
  
  
  m_post = c(
    # Post measurement
    16.7, 11.6, 
    20.7, 18.1, 
    14.5, 16., 
    11.6, 10.4,
    22.6, 30.9,
    
    # 6 months measurement
    17.8, 13.2,
    21.5, 19.1,
    15.3, 16.,
    11.7, 11.5,
    22.6, 29.6),
  
  sd_post = c(
    # Post measurement
    10.3, 9.3, 
    6.3, 6.9,
    7.0, 7.2, 
    5.4, 5.3,
    13.1, 12.3,
    
    # 6 months measurement
    11., 11.2,
    6.4, 6.3,
    8., 7.4,
    5.2, 5.7,
    14.5, 13.3)
  
  ) |> 
relocate(outcome, .after = group); Jacob2010
## # A tibble: 20 × 9
##    group        outcome          timing     N N_start m_pre sd_pre m_post
##    <fct>        <chr>            <chr>  <dbl>   <dbl> <dbl>  <dbl>  <dbl>
##  1 Intervention RSES             Post      19      19  13.3    8.6   16.7
##  2 Control      RSES             Post      24      24  13.3    9.9   11.6
##  3 Intervention SSES Achievement Post      19      19  18.7    4     20.7
##  4 Control      SSES Achievement Post      24      24  19.2    6.6   18.1
##  5 Intervention SSES Social      Post      19      19  13.8    6.6   14.5
##  6 Control      SSES Social      Post      24      24  16.3    7.4   16  
##  7 Intervention SSES Appearance  Post      19      19  11.7    5.7   11.6
##  8 Control      SSES Appearance  Post      24      24  10.8    5.2   10.4
##  9 Intervention BDI              Post      19      19  27.8   11.8   22.6
## 10 Control      BDI              Post      24      24  30     12.2   30.9
## 11 Intervention RSES             6m        19      19  13.3    8.6   17.8
## 12 Control      RSES             6m        24      24  13.3    9.9   13.2
## 13 Intervention SSES Achievement 6m        19      19  18.7    4     21.5
## 14 Control      SSES Achievement 6m        24      24  19.2    6.6   19.1
## 15 Intervention SSES Social      6m        19      19  13.8    6.6   15.3
## 16 Control      SSES Social      6m        24      24  16.3    7.4   16  
## 17 Intervention SSES Appearance  6m        19      19  11.7    5.7   11.7
## 18 Control      SSES Appearance  6m        24      24  10.8    5.2   11.5
## 19 Intervention BDI              6m        19      19  27.8   11.8   22.6
## 20 Control      BDI              6m        24      24  30     12.2   29.6
##    sd_post
##      <dbl>
##  1    10.3
##  2     9.3
##  3     6.3
##  4     6.9
##  5     7  
##  6     7.2
##  7     5.4
##  8     5.3
##  9    13.1
## 10    12.3
## 11    11  
## 12    11.2
## 13     6.4
## 14     6.3
## 15     8  
## 16     7.4
## 17     5.2
## 18     5.7
## 19    14.5
## 20    13.3
# Making the druss2018 tibble wide in order to estimate the effect sizes and
# further analysis. 

# Turning data into wide format
Jacob2010_wide <- 
  Jacob2010 |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )   


Jacob2010_est <- 
  Jacob2010_wide |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "BDI") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "RSES|SSES") ~ "Self-esteem",
      .default = NA_character_
    ),
    
    study = c("Jacob et al. 2010"),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Reverting all outcomes
   # Reverting m_post for specific outcomes
    m_post = if_else(outcome == "BDI", (m_post_t - m_post_c) * -1, m_post_t - m_post_c),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
   
   
    # Reverting m_post for specific outcomes: BDI
    m_diff_t = if_else(outcome == "BDI",
                       (m_post_t - m_pre_t)*-1,
                        m_post_t - m_pre_t),
    m_diff_c = if_else(outcome == "BDI",
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)  + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) ,
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)  + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD 
    
  ) |> 
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # Average cluster size in treatment group
    # Group size was between 5 to 7 (p. 379)
    
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 379)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
  ) |> 
  ungroup() |> 
  mutate(vary_id = paste0(outcome, "/", timing));  Jacob2010_est
## # A tibble: 10 × 65
##    outcome          timing   N_t   N_c N_start_t N_start_c m_pre_t m_pre_c
##    <chr>            <chr>  <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>
##  1 RSES             Post      19    24        19        24    13.3    13.3
##  2 SSES Achievement Post      19    24        19        24    18.7    19.2
##  3 SSES Social      Post      19    24        19        24    13.8    16.3
##  4 SSES Appearance  Post      19    24        19        24    11.7    10.8
##  5 BDI              Post      19    24        19        24    27.8    30  
##  6 RSES             6m        19    24        19        24    13.3    13.3
##  7 SSES Achievement 6m        19    24        19        24    18.7    19.2
##  8 SSES Social      6m        19    24        19        24    13.8    16.3
##  9 SSES Appearance  6m        19    24        19        24    11.7    10.8
## 10 BDI              6m        19    24        19        24    27.8    30  
##    sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##       <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
##  1      8.6      9.9     16.7     11.6      10.3       9.3
##  2      4        6.6     20.7     18.1       6.3       6.9
##  3      6.6      7.4     14.5     16         7         7.2
##  4      5.7      5.2     11.6     10.4       5.4       5.3
##  5     11.8     12.2     22.6     30.9      13.1      12.3
##  6      8.6      9.9     17.8     13.2      11        11.2
##  7      4        6.6     21.5     19.1       6.4       6.3
##  8      6.6      7.4     15.3     16         8         7.4
##  9      5.7      5.2     11.7     11.5       5.2       5.7
## 10     11.8     12.2     22.6     29.6      14.5      13.3
##    analysis_plan                         study             effect_size
##    <chr>                                 <chr>             <chr>      
##  1 Self-esteem                           Jacob et al. 2010 SMD        
##  2 Self-esteem                           Jacob et al. 2010 SMD        
##  3 Self-esteem                           Jacob et al. 2010 SMD        
##  4 Self-esteem                           Jacob et al. 2010 SMD        
##  5 All mental health outcomes/Depression Jacob et al. 2010 SMD        
##  6 Self-esteem                           Jacob et al. 2010 SMD        
##  7 Self-esteem                           Jacob et al. 2010 SMD        
##  8 Self-esteem                           Jacob et al. 2010 SMD        
##  9 Self-esteem                           Jacob et al. 2010 SMD        
## 10 All mental health outcomes/Depression Jacob et al. 2010 SMD        
##    sd_used             main_es_method    ppcor ppcor_method N_total df_ind
##    <chr>               <chr>             <dbl> <chr>          <dbl>  <dbl>
##  1 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           43     43
##  2 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           43     43
##  3 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           43     43
##  4 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           43     43
##  5 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           43     43
##  6 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           43     43
##  7 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           43     43
##  8 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           43     43
##  9 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           43     43
## 10 Pooled posttest SDs Raw diff-in-diffs   0.5 Imputed           43     43
##     m_post sd_pool   d_post vd_post Wd_post      J   g_post vg_post Wg_post
##      <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>   <dbl>
##  1  5.1      9.752  0.5230  0.09748 0.09430 0.9825  0.5138  0.09737 0.09430
##  2  2.600    6.643  0.3914  0.09608 0.09430 0.9825  0.3845  0.09602 0.09430
##  3 -1.5      7.113 -0.2109  0.09482 0.09430 0.9825 -0.2072  0.09480 0.09430
##  4  1.200    5.344  0.2245  0.09488 0.09430 0.9825  0.2206  0.09486 0.09430
##  5  8.3     12.66   0.6557  0.09930 0.09430 0.9825  0.6442  0.09912 0.09430
##  6  4.6     11.11   0.4139  0.09629 0.09430 0.9825  0.4067  0.09622 0.09430
##  7  2.400    6.344  0.3783  0.09596 0.09430 0.9825  0.3717  0.09590 0.09430
##  8 -0.7000   7.669 -0.09127 0.09440 0.09430 0.9825 -0.08967 0.09439 0.09430
##  9  0.2000   5.486  0.03646 0.09431 0.09430 0.9825  0.03582 0.09431 0.09430
## 10  7       13.84   0.5058  0.09727 0.09430 0.9825  0.4969  0.09717 0.09430
##    m_diff_t m_diff_c     d_DD   vd_DD   Wd_DD     g_DD   vg_DD   Wg_DD
##       <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
##  1   3.4     -1.700   0.5230  0.09748 0.09430  0.5138  0.09737 0.09430
##  2   2       -1.100   0.4666  0.09683 0.09430  0.4585  0.09674 0.09430
##  3   0.7000  -0.3000  0.1406  0.09453 0.09430  0.1381  0.09452 0.09430
##  4  -0.1000  -0.4000  0.05614 0.09433 0.09430  0.05515 0.09433 0.09430
##  5   5.2     -0.9000  0.4819  0.09700 0.09430  0.4735  0.09690 0.09430
##  6   4.5     -0.1000  0.4139  0.09629 0.09430  0.4067  0.09622 0.09430
##  7   2.8     -0.1000  0.4571  0.09673 0.09430  0.4491  0.09664 0.09430
##  8   1.5     -0.3000  0.2347  0.09494 0.09430  0.2306  0.09492 0.09430
##  9   0        0.7000 -0.1276  0.09449 0.09430 -0.1254  0.09448 0.09430
## 10   5.2      0.4000  0.3468  0.09570 0.09430  0.3407  0.09565 0.09430
##    avg_cl_size avg_cl_type           icc icc_type n_covariates gamma_sqrt df_adj
##          <dbl> <chr>               <dbl> <chr>           <dbl>      <dbl>  <dbl>
##  1           6 From study (p. 379)   0.1 Imputed             1      0.965  40.17
##  2           6 From study (p. 379)   0.1 Imputed             1      0.965  40.17
##  3           6 From study (p. 379)   0.1 Imputed             1      0.965  40.17
##  4           6 From study (p. 379)   0.1 Imputed             1      0.965  40.17
##  5           6 From study (p. 379)   0.1 Imputed             1      0.965  40.17
##  6           6 From study (p. 379)   0.1 Imputed             1      0.965  40.17
##  7           6 From study (p. 379)   0.1 Imputed             1      0.965  40.17
##  8           6 From study (p. 379)   0.1 Imputed             1      0.965  40.17
##  9           6 From study (p. 379)   0.1 Imputed             1      0.965  40.17
## 10           6 From study (p. 379)   0.1 Imputed             1      0.965  40.17
##     omega  gt_post vgt_post Wgt_post    gt_DD vgt_DD Wgt_DD    hg_DD  vhg_DD
##     <dbl>    <dbl>    <dbl>    <dbl>    <dbl>  <dbl>  <dbl>    <dbl>   <dbl>
##  1 0.9812  0.4952    0.1195   0.1165  0.4952  0.1195 0.1165  0.2280  0.02489
##  2 0.9812  0.3706    0.1182   0.1165  0.4418  0.1189 0.1165  0.2036  0.02489
##  3 0.9812 -0.1997    0.1170   0.1165  0.1331  0.1167 0.1165  0.06153 0.02489
##  4 0.9812  0.2126    0.1170   0.1165  0.05315 0.1165 0.1165  0.02457 0.02489
##  5 0.9812  0.6209    0.1213   0.1165  0.4563  0.1191 0.1165  0.2102  0.02489
##  6 0.9812  0.3920    0.1184   0.1165  0.3920  0.1184 0.1165  0.1807  0.02489
##  7 0.9812  0.3582    0.1181   0.1165  0.4328  0.1188 0.1165  0.1995  0.02489
##  8 0.9812 -0.08642   0.1166   0.1165  0.2222  0.1171 0.1165  0.1027  0.02489
##  9 0.9812  0.03452   0.1165   0.1165 -0.1208  0.1166 0.1165 -0.05584 0.02489
## 10 0.9812  0.4789    0.1193   0.1165  0.3284  0.1178 0.1165  0.1515  0.02489
##     h_DD df_DD n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop
##    <dbl> <dbl>           <dbl> <chr>             <dbl>     <dbl>      <dbl>
##  1 40.17 40.17               1 eta               1.235   NA         NA     
##  2 40.17 40.17               1 eta               1.235   NA         NA     
##  3 40.17 40.17               1 eta               1.235   NA         NA     
##  4 40.17 40.17               1 eta               1.235   NA         NA     
##  5 40.17 40.17               1 eta               1.235    0.4822     0.1135
##  6 40.17 40.17               1 eta               1.235   NA         NA     
##  7 40.17 40.17               1 eta               1.235   NA         NA     
##  8 40.17 40.17               1 eta               1.235   NA         NA     
##  9 40.17 40.17               1 eta               1.235   NA         NA     
## 10 40.17 40.17               1 eta               1.235    0.3794     0.1134
##    Wgt_DD_pop vary_id              
##         <dbl> <chr>                
##  1    NA      RSES/Post            
##  2    NA      SSES Achievement/Post
##  3    NA      SSES Social/Post     
##  4    NA      SSES Appearance/Post 
##  5     0.1132 BDI/Post             
##  6    NA      RSES/6m              
##  7    NA      SSES Achievement/6m  
##  8    NA      SSES Social/6m       
##  9    NA      SSES Appearance/6m   
## 10     0.1132 BDI/6m

James et al. (2004) (Quality-checked)

Entering data from Table 2 (p. 988)

James2004 <- tibble(
  group = rep(c("Treatment", "Control"), each = 1,5),
 
  outcome =  rep(c("BPRS", "BSI-Global","DAST", "AUDIT","SDS"
  ), each = 2,1), 
  
  N = c(
    29, 29,
    31, 29,
    29,29,
    29, 29,
    28, 29
    
  ), 
  
  N_start = rep(c(
    32,31
  ), each = 1,5),
  
  m_pre = c(
    35.41, 33.93, # BPRS
    1.36, 1.27, # BSI-Global
    11.58,  9.10, # DAST
    11.65, 12.65, # AUDIT
    7.42, 6.75    # SDS
  ), 
  sd_pre = c(
    11.08, 8.61,
    0.88, 0.63,
    4.82, 4.66,
    10.19, 8.22,
    3.63, 3.46
  ),
  m_post = c(
    30.48, 38.10,
    0.87, 1.12,
    4.96, 8.24,
    8.34, 11.65,
    3.85, 5.20
  ),
  sd_post = c(
    10.68, 11.54,
    0.63, 0.77,
    4.49, 5.31, 
    8.35, 7.35,
    3.30, 3.85
  )
)


# Turning data into wide format
James2004_wide <- James2004 |> 
      mutate(group = case_match(group, "Treatment" ~ "t", "Control" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
      ); James2004_wide
## # A tibble: 5 × 13
##   outcome      N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t sd_pre_c
##   <chr>      <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 BPRS          29    29        32        31   35.41   33.93    11.08     8.61
## 2 BSI-Global    31    29        32        31    1.36    1.27     0.88     0.63
## 3 DAST          29    29        32        31   11.58    9.1      4.82     4.66
## 4 AUDIT         29    29        32        31   11.65   12.65    10.19     8.22
## 5 SDS           28    29        32        31    7.42    6.75     3.63     3.46
##   m_post_t m_post_c sd_post_t sd_post_c
##      <dbl>    <dbl>     <dbl>     <dbl>
## 1    30.48    38.1      10.68     11.54
## 2     0.87     1.12      0.63      0.77
## 3     4.96     8.24      4.49      5.31
## 4     8.34    11.65      8.35      7.35
## 5     3.85     5.2       3.3       3.85
James2004_est <- James2004_wide |> 
  mutate(
    
    study = "James et al. 2004",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    analysis_plan = case_when(
      str_detect(outcome, "BPRS|BSI-Global") ~ "All mental health outcomes",
      str_detect(outcome, "DAST|AUDIT|SDS") ~ "Alcohol and drug abuse/misuse",
      .default = NA_character_
    ),
    
    # F-values taken from table 2, p. 988. (Not used. Yields larger varainces)
    F_val = c(
      7.364, 0.865, 16.221, 1.352, 2.205
    ),
    
    ppcor = ppcor_imp,  
    ppcor_method = "Imputed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    # For all scores lower are benefical
    m_post = (m_post_t - m_post_c)*-1,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)
    
  ) |> 
rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group was approx. 6, p. 985
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 985)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
      n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
   
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
  
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
       vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); James2004_est
## # A tibble: 5 × 62
##   outcome      N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t sd_pre_c
##   <chr>      <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 BPRS          29    29        32        31   35.41   33.93    11.08     8.61
## 2 BSI-Global    31    29        32        31    1.36    1.27     0.88     0.63
## 3 DAST          29    29        32        31   11.58    9.1      4.82     4.66
## 4 AUDIT         29    29        32        31   11.65   12.65    10.19     8.22
## 5 SDS           28    29        32        31    7.42    6.75     3.63     3.46
##   m_post_t m_post_c sd_post_t sd_post_c study             effect_size
##      <dbl>    <dbl>     <dbl>     <dbl> <chr>             <chr>      
## 1    30.48    38.1      10.68     11.54 James et al. 2004 SMD        
## 2     0.87     1.12      0.63      0.77 James et al. 2004 SMD        
## 3     4.96     8.24      4.49      5.31 James et al. 2004 SMD        
## 4     8.34    11.65      8.35      7.35 James et al. 2004 SMD        
## 5     3.85     5.2       3.3       3.85 James et al. 2004 SMD        
##   sd_used             main_es_method    analysis_plan                  F_val
##   <chr>               <chr>             <chr>                          <dbl>
## 1 Pooled posttest SDs Raw diff-in-diffs All mental health outcomes     7.364
## 2 Pooled posttest SDs Raw diff-in-diffs All mental health outcomes     0.865
## 3 Pooled posttest SDs Raw diff-in-diffs Alcohol and drug abuse/misuse 16.22 
## 4 Pooled posttest SDs Raw diff-in-diffs Alcohol and drug abuse/misuse  1.352
## 5 Pooled posttest SDs Raw diff-in-diffs Alcohol and drug abuse/misuse  2.205
##   ppcor ppcor_method N_total df_ind m_post sd_pool d_post vd_post Wd_post      J
##   <dbl> <chr>          <dbl>  <dbl>  <dbl>   <dbl>  <dbl>   <dbl>   <dbl>  <dbl>
## 1   0.5 Imputed           58     58   7.62 11.12   0.6854 0.07301 0.06897 0.9870
## 2   0.5 Imputed           60     60   0.25  0.7011 0.3566 0.06780 0.06674 0.9874
## 3   0.5 Imputed           58     58   3.28  4.917  0.6671 0.07280 0.06897 0.9870
## 4   0.5 Imputed           58     58   3.31  7.866  0.4208 0.07049 0.06897 0.9870
## 5   0.5 Imputed           57     57   1.35  3.591  0.3760 0.07144 0.07020 0.9868
##   g_post vg_post Wg_post m_diff_t m_diff_c   d_DD   vd_DD   Wd_DD   g_DD   vg_DD
##    <dbl>   <dbl>   <dbl>    <dbl>    <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl>
## 1 0.6765 0.07291 0.06897    4.930  -4.17   0.8185 0.07474 0.06897 0.8078 0.07459
## 2 0.3521 0.06777 0.06674    0.49    0.1500 0.4850 0.06870 0.06674 0.4789 0.06865
## 3 0.6584 0.07270 0.06897    6.62    0.8600 1.171  0.08079 0.06897 1.156  0.08049
## 4 0.4153 0.07045 0.06897    3.31    1      0.2937 0.06971 0.06897 0.2899 0.06969
## 5 0.3710 0.07140 0.07020    3.57    1.55   0.5626 0.07297 0.07020 0.5552 0.07290
##     Wg_DD avg_cl_size avg_cl_type           icc icc_type n_covariates gamma_sqrt
##     <dbl>       <dbl> <chr>               <dbl> <chr>           <dbl>      <dbl>
## 1 0.06897           6 From study (p. 985)   0.1 Imputed             1      0.97 
## 2 0.06674           6 From study (p. 985)   0.1 Imputed             1      0.971
## 3 0.06897           6 From study (p. 985)   0.1 Imputed             1      0.97 
## 4 0.06897           6 From study (p. 985)   0.1 Imputed             1      0.97 
## 5 0.07020           6 From study (p. 985)   0.1 Imputed             1      0.97 
##   df_adj  omega gt_post vgt_post Wgt_post  gt_DD  vgt_DD  Wgt_DD  hg_DD  vhg_DD
##    <dbl>  <dbl>   <dbl>    <dbl>    <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl>
## 1  54.24 0.9861  0.6556  0.08669  0.08276 0.7829 0.08837 0.08276 0.3643 0.01832
## 2  56.08 0.9866  0.3416  0.08045  0.07942 0.4646 0.08133 0.07942 0.2185 0.01771
## 3  54.24 0.9861  0.6381  0.08649  0.08276 1.120  0.09426 0.08276 0.5157 0.01832
## 4  54.24 0.9861  0.4025  0.08424  0.08276 0.2809 0.08348 0.08276 0.1320 0.01832
## 5  53.31 0.9859  0.3596  0.08579  0.08459 0.5380 0.08729 0.08459 0.2512 0.01864
##    h_DD df_DD n_covariates_DD adj_fct_DD adj_value_DD vary_id   
##   <dbl> <dbl>           <dbl> <chr>             <dbl> <chr>     
## 1 54.59 54.59               1 eta               1.2   BPRS      
## 2 56.48 56.48               1 eta               1.19  BSI-Global
## 3 54.59 54.59               1 eta               1.2   DAST      
## 4 54.59 54.59               1 eta               1.2   AUDIT     
## 5 53.64 53.64               1 eta               1.205 SDS

Kanie et al. (2019) (Quality-checked)

Entering data from Table 4 (p. 8)

# Taken from table 4, p. 8
Kanie2019 <- tibble(
  group = as.factor(rep(c("SCIT", "TAU"), each = 1,5)), 
  
  timing = "Post", 
  
  outcome = rep(c(
    "SCSQ total", # Social Cognition Screening Questionnaire
    "BACS", # Brief Assessment of Cognition in Schizophrenia
    "PANSS total", # Positive and Negative Syndrome Scale
    "GAF", # Global Assessment of Functioning
    "SFS" # Social Functioning Scale
  ),  each = 2,1), 
  
  N = rep(c(32, 29), 5),            
  
  N_start = 36,
  
  m_pre = c(
    31.04, 32.83, # Social Cognition Screening Questionnaire
    -1.64, -1.33, # Brief Assessment of Cognition in Schizophrenia
    64.78, 63.78, # Positive and Negative Syndrome Scale
    51.59, 53.44, # Global Assessment of Functioning
    116.06, 106.53 # Social Functioning Scale
    ), 
    
  sd_pre = c(
    3.58, 3.47, 
    1.01, 1.42, 
    18.18, 18.98, 
    8.81, 9.49,
    23.95, 23.47), 
  
  m_post = c(
    # 3 months measurement - This is a midterm measurement/during-the-intervention measure 
  #  32.94, 33.45, 
  #  -1.37, -1.16, 
  #  61.34, 59.94, 
  #  52.25, 58.06,
  #  118.44, 107.41,
    
    
    # 6 months measurement
    32.79, 33.4,
    -1.13, -0.77,
    60.03, 59.81,
    56.09, 55.59,
    117.45, 108.91
    ),
  
  sd_post = c(
    # 3 months measurement This is a midterm measurement/during-the-intervention measure
  #  3.2, 2.63, 
  #  1.09, 1.36,
  #  19.15, 18.02, 
  #  13.63, 13.47,
  #  25.31, 28.03,
    
    # 6 months measurement
    3.17, 2.63,
    1.22, 1.35,
    18.68, 17.32,
    15.33, 14.5,
    25.42, 26.6
    )
  
); Kanie2019
## # A tibble: 10 × 9
##    group timing outcome         N N_start  m_pre sd_pre m_post sd_post
##    <fct> <chr>  <chr>       <dbl>   <dbl>  <dbl>  <dbl>  <dbl>   <dbl>
##  1 SCIT  Post   SCSQ total     32      36  31.04   3.58  32.79    3.17
##  2 TAU   Post   SCSQ total     29      36  32.83   3.47  33.4     2.63
##  3 SCIT  Post   BACS           32      36  -1.64   1.01  -1.13    1.22
##  4 TAU   Post   BACS           29      36  -1.33   1.42  -0.77    1.35
##  5 SCIT  Post   PANSS total    32      36  64.78  18.18  60.03   18.68
##  6 TAU   Post   PANSS total    29      36  63.78  18.98  59.81   17.32
##  7 SCIT  Post   GAF            32      36  51.59   8.81  56.09   15.33
##  8 TAU   Post   GAF            29      36  53.44   9.49  55.59   14.5 
##  9 SCIT  Post   SFS            32      36 116.1   23.95 117.4    25.42
## 10 TAU   Post   SFS            29      36 106.5   23.47 108.9    26.6
# Turning data into wide format
Kanie2019_wide <- Kanie2019 |> 
      mutate(group = case_match(group, "SCIT" ~ "t", "TAU" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
      ) |> 
  mutate(
    es_paper = c(0.33, -0.05, -0.04, 0.26, -0.04),
    es_paper_type = "DiD"
  ); Kanie2019_wide
## # A tibble: 5 × 16
##   timing outcome       N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t
##   <chr>  <chr>       <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>
## 1 Post   SCSQ total     32    29        36        36   31.04   32.83     3.58
## 2 Post   BACS           32    29        36        36   -1.64   -1.33     1.01
## 3 Post   PANSS total    32    29        36        36   64.78   63.78    18.18
## 4 Post   GAF            32    29        36        36   51.59   53.44     8.81
## 5 Post   SFS            32    29        36        36  116.1   106.5     23.95
##   sd_pre_c m_post_t m_post_c sd_post_t sd_post_c es_paper es_paper_type
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl> <chr>        
## 1     3.47    32.79    33.4       3.17      2.63     0.33 DiD          
## 2     1.42    -1.13    -0.77      1.22      1.35    -0.05 DiD          
## 3    18.98    60.03    59.81     18.68     17.32    -0.04 DiD          
## 4     9.49    56.09    55.59     15.33     14.5      0.26 DiD          
## 5    23.47   117.4    108.9      25.42     26.6     -0.04 DiD
Kanie2019_est <- 
  Kanie2019_wide |> 
  mutate(
    
    study = "Kanie et al. 2019",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    analysis_plan = case_when(
      str_detect(outcome, "SCSQ") ~ "Unused",
      str_detect(outcome, "BACS") ~ "All mental health outcomes",
      str_detect(outcome, "SFS|GAF") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "PANSS") ~ "All mental health outcomes/Symptoms of psychosis",
      .default = NA_character_
    ),

    # ppcor is imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    # For PANSS: reverted
    m_post = if_else(
      outcome != "PANSS total", 
      (m_post_t - m_post_c)*-1,
      m_post_t - m_post_c
    ),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For PANSS: reverted
    m_diff_t = if_else(
      outcome != "PANSS total",
      (m_post_t - m_pre_t)*-1,
      m_post_t - m_pre_t
    ),
    m_diff_c = if_else(
      outcome != "PANSS total",
      (m_post_c - m_pre_c)*-1,
      m_post_c - m_pre_c
    ),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |>
  mutate(
    
    # Average cluster size in treatment group
    # Group size was between 4 to 8 (p. 3)
    
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 3)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * ((m_diff_t - m_diff_c)/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste0(outcome)
  ) |> 
  ungroup(); Kanie2019_est
## # A tibble: 5 × 67
##   timing outcome       N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t
##   <chr>  <chr>       <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>
## 1 Post   SCSQ total     32    29        36        36   31.04   32.83     3.58
## 2 Post   BACS           32    29        36        36   -1.64   -1.33     1.01
## 3 Post   PANSS total    32    29        36        36   64.78   63.78    18.18
## 4 Post   GAF            32    29        36        36   51.59   53.44     8.81
## 5 Post   SFS            32    29        36        36  116.1   106.5     23.95
##   sd_pre_c m_post_t m_post_c sd_post_t sd_post_c es_paper es_paper_type
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl> <chr>        
## 1     3.47    32.79    33.4       3.17      2.63     0.33 DiD          
## 2     1.42    -1.13    -0.77      1.22      1.35    -0.05 DiD          
## 3    18.98    60.03    59.81     18.68     17.32    -0.04 DiD          
## 4     9.49    56.09    55.59     15.33     14.5      0.26 DiD          
## 5    23.47   117.4    108.9      25.42     26.6     -0.04 DiD          
##   study             effect_size sd_used            main_es_method   
##   <chr>             <chr>       <chr>              <chr>            
## 1 Kanie et al. 2019 SMD         Pooled posttest SD Raw diff-in-diffs
## 2 Kanie et al. 2019 SMD         Pooled posttest SD Raw diff-in-diffs
## 3 Kanie et al. 2019 SMD         Pooled posttest SD Raw diff-in-diffs
## 4 Kanie et al. 2019 SMD         Pooled posttest SD Raw diff-in-diffs
## 5 Kanie et al. 2019 SMD         Pooled posttest SD Raw diff-in-diffs
##   analysis_plan                                    ppcor ppcor_method N_total
##   <chr>                                            <dbl> <chr>          <dbl>
## 1 Unused                                             0.5 Imputed           61
## 2 All mental health outcomes                         0.5 Imputed           61
## 3 All mental health outcomes/Symptoms of psychosis   0.5 Imputed           61
## 4 Social functioning (degree of impairment)          0.5 Imputed           61
## 5 Social functioning (degree of impairment)          0.5 Imputed           61
##   df_ind  m_post sd_pool   d_post vd_post Wd_post      J   g_post vg_post
##    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>
## 1     61  0.6100   2.926  0.2085  0.06609 0.06573 0.9877  0.2059  0.06608
## 2     61  0.36     1.283  0.2805  0.06638 0.06573 0.9877  0.2771  0.06636
## 3     61  0.2200  18.05   0.01219 0.06573 0.06573 0.9877  0.01204 0.06573
## 4     61 -0.5     14.94  -0.03346 0.06574 0.06573 0.9877 -0.03305 0.06574
## 5     61 -8.540   25.99  -0.3286  0.06662 0.06573 0.9877 -0.3246  0.06660
##   Wg_post m_diff_t m_diff_c     d_DD   vd_DD   Wd_DD     g_DD   vg_DD   Wg_DD
##     <dbl>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
## 1 0.06573    -1.75   -0.57  -0.4033  0.06707 0.06573 -0.3983  0.06703 0.06573
## 2 0.06573    -0.51   -0.56   0.03896 0.06575 0.06573  0.03848 0.06574 0.06573
## 3 0.06573    -4.75   -3.97  -0.04322 0.06575 0.06573 -0.04269 0.06575 0.06573
## 4 0.06573    -4.5    -2.150 -0.1573  0.06594 0.06573 -0.1553  0.06593 0.06573
## 5 0.06573    -1.39   -2.380  0.03810 0.06574 0.06573  0.03763 0.06574 0.06573
##   avg_cl_size avg_cl_type         icc icc_type n_covariates gamma_sqrt df_adj
##         <dbl> <chr>             <dbl> <chr>           <dbl>      <dbl>  <dbl>
## 1           6 From study (p. 3)   0.1 Imputed             1      0.972  57.42
## 2           6 From study (p. 3)   0.1 Imputed             1      0.972  57.42
## 3           6 From study (p. 3)   0.1 Imputed             1      0.972  57.42
## 4           6 From study (p. 3)   0.1 Imputed             1      0.972  57.42
## 5           6 From study (p. 3)   0.1 Imputed             1      0.972  57.42
##    omega  gt_post vgt_post Wgt_post    gt_DD  vgt_DD  Wgt_DD    hg_DD  vhg_DD
##    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>
## 1 0.9869  0.2000   0.07824  0.07789 -0.3868  0.07920 0.07789 -0.1824  0.01742
## 2 0.9869  0.2691   0.07852  0.07789  0.03737 0.07791 0.07789  0.01767 0.01742
## 3 0.9869  0.01169  0.07789  0.07789 -0.04146 0.07791 0.07789 -0.01960 0.01742
## 4 0.9869 -0.03210  0.07790  0.07789 -0.1509  0.07809 0.07789 -0.07131 0.01742
## 5 0.9869 -0.3152   0.07876  0.07789  0.03654 0.07790 0.07789  0.01728 0.01742
##    h_DD df_DD n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop
##   <dbl> <dbl>           <dbl> <chr>             <dbl>     <dbl>      <dbl>
## 1 57.42 57.42               1 eta               1.185   NA        NA      
## 2 57.42 57.42               1 eta               1.185   NA        NA      
## 3 57.42 57.42               1 eta               1.185   NA        NA      
## 4 57.42 57.42               1 eta               1.185   -0.1770    0.07452
## 5 57.42 57.42               1 eta               1.185   NA        NA      
##   Wgt_DD_pop vary_id    
##        <dbl> <chr>      
## 1   NA       SCSQ total 
## 2   NA       BACS       
## 3   NA       PANSS total
## 4    0.07444 GAF        
## 5   NA       SFS

Lim et al. (2020) (Quality-checked)

Entering data from Table 2 (p. 5)

# Data from table 2 (p.5)  containing means, standard deviation


Lim2020 <- tibble(
  group = as.factor(rep(c("Social Cognitive Skills Training",
                          "Treatment as Usual"), each = 1,6)),
  
  outcome = as.factor(rep(c(
                            "Social Functioning (QLS)",
                            "PANSS Negative",
                            "PANSS Excitement",
                            "PANSS Cognitive",
                            "PANSS Positive",
                            "PANSS Depressive"
                            ), each = 2,1 )
                      ),
  
  N = rep(c(18, 21), 6),
  N_start = rep(c(21, 24), 6),
  
  
  m_pre = c(46.15, 61.74, # Social Functioning (QLS) total
           18.22, 16.57,  # PANSS Negative
           6.28, 6.91,    # PANSS Excitement
           13.94,13.57,   # PANSS Cognitive
           7.67, 9.62,    # PANSS Positive
           8.67, 11.29    # PANSS Depressive
           
    ), 
  
  sd_pre = c(12.01, 18.60, # Social Functioning (QLS) total
             6.23, 3.84,   # PANSS Negative
             1.87, 2.02,   # PANSS Excitement
             4.67, 3.25,   # PANSS Cognitive
             2.00, 3.54,   # PANSS Positive
             2.47, 3.45    # PANSS Depressive
  ),
  
  m_post = c(54.76, 57.70, # Social Functioning (QLS) total
             16.89, 18.14, # PANSS Negative
             6.89, 7.33,   # PANSS Excitement
             13.44, 15.19, # PANSS Cognitive
             8.72, 8.72,   # PANSS Positive
             8.94, 13.81   # PANSS Depressive
    ),
  
  sd_post = c(10.88, 15.92, # Social Functioning (QLS) total
              4.51, 3.71,   # PANSS Negative
              2.05, 2.20,   # PANSS Excitement
              2.75, 2.98,   # PANSS Cognitive
              3.06, 2.67,   # PANSS Positive
              2.98, 4.21    # PANSS Depressive
    
  )
)

# Making the lim2020 tibble wide in order to estimate the effect sizes and
# further analysis. 


Lim2020_wide <-
  Lim2020 |> 
  mutate (group = case_match(
    group, "Social Cognitive Skills Training"  ~ "t", 
           "Treatment as Usual" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


# Effect size calculating 
Lim2020_est <-           
  Lim2020_wide |>
  mutate(
    
    study = "Lim et al. 2020",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    analysis_plan = case_when(
      str_detect(outcome, "QLS") ~ "Wellbeing and Quality of Life",
      str_detect(outcome, "Negative") ~ "All mental health outcomes/Symptoms of psychosis",
      
      .default = "All mental health outcomes/Symptoms of psychosis"
    ),
    
    # ppcor is imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    # F-Values - based on ANOVA - from table 2 (p.5)
    #F_val = c(3.116, 5.157, 0.085, 5.055, 1.528, 8.487),
    
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # For Social functioning lower scores is beneficial why these is reverted
    m_post = if_else(outcome != "Social Functioning (QLS)", (m_post_t - m_post_c)*-1,
                     m_post_t - m_post_c), 
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    m_diff_t = if_else(outcome != "Social Functioning (QLS)", 
                       (m_post_t - m_pre_t) * -1,
                       m_post_t - m_pre_t),
    
    m_diff_c = if_else(outcome != "Social Functioning (QLS)",
                       (m_post_c - m_pre_c) * -1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
    
  ) |>
  rowwise() |> 
  mutate(
    
    icc = ICC_01,
    icc_type = "Imputed",
    
    # Average cluster size in treatment group
    # We don't know the average group size. Therefore, we impute this value
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
       vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor  = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    vary_id = outcome
  ) |> 
  ungroup(); Lim2020_est
## # A tibble: 6 × 61
##   outcome                    N_t   N_c N_start_t N_start_c m_pre_t m_pre_c
##   <fct>                    <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>
## 1 Social Functioning (QLS)    18    21        21        24   46.15   61.74
## 2 PANSS Negative              18    21        21        24   18.22   16.57
## 3 PANSS Excitement            18    21        21        24    6.28    6.91
## 4 PANSS Cognitive             18    21        21        24   13.94   13.57
## 5 PANSS Positive              18    21        21        24    7.67    9.62
## 6 PANSS Depressive            18    21        21        24    8.67   11.29
##   sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c study          
##      <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl> <chr>          
## 1    12.01    18.6     54.76    57.7      10.88     15.92 Lim et al. 2020
## 2     6.23     3.84    16.89    18.14      4.51      3.71 Lim et al. 2020
## 3     1.87     2.02     6.89     7.33      2.05      2.2  Lim et al. 2020
## 4     4.67     3.25    13.44    15.19      2.75      2.98 Lim et al. 2020
## 5     2        3.54     8.72     8.72      3.06      2.67 Lim et al. 2020
## 6     2.47     3.45     8.94    13.81      2.98      4.21 Lim et al. 2020
##   effect_size sd_used            main_es_method   
##   <chr>       <chr>              <chr>            
## 1 SMD         Pooled posttest SD Raw diff-in-diffs
## 2 SMD         Pooled posttest SD Raw diff-in-diffs
## 3 SMD         Pooled posttest SD Raw diff-in-diffs
## 4 SMD         Pooled posttest SD Raw diff-in-diffs
## 5 SMD         Pooled posttest SD Raw diff-in-diffs
## 6 SMD         Pooled posttest SD Raw diff-in-diffs
##   analysis_plan                                    ppcor ppcor_method N_total
##   <chr>                                            <dbl> <chr>          <dbl>
## 1 Wellbeing and Quality of Life                      0.5 Imputed           39
## 2 All mental health outcomes/Symptoms of psychosis   0.5 Imputed           39
## 3 All mental health outcomes/Symptoms of psychosis   0.5 Imputed           39
## 4 All mental health outcomes/Symptoms of psychosis   0.5 Imputed           39
## 5 All mental health outcomes/Symptoms of psychosis   0.5 Imputed           39
## 6 All mental health outcomes/Symptoms of psychosis   0.5 Imputed           39
##   df_ind  m_post sd_pool  d_post vd_post Wd_post      J  g_post vg_post Wg_post
##    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>
## 1     39 -2.940   13.83  -0.2125  0.1038  0.1032 0.9806 -0.2084  0.1037  0.1032
## 2     39  1.25     4.097  0.3051  0.1044  0.1032 0.9806  0.2992  0.1043  0.1032
## 3     39  0.4400   2.132  0.2063  0.1037  0.1032 0.9806  0.2023  0.1037  0.1032
## 4     39  1.75     2.877  0.6084  0.1079  0.1032 0.9806  0.5966  0.1077  0.1032
## 5     39  0        2.856  0       0.1032  0.1032 0.9806  0       0.1032  0.1032
## 6     39  4.87     3.696  1.318   0.1254  0.1032 0.9806  1.292   0.1246  0.1032
##   m_diff_t m_diff_c     d_DD  vd_DD  Wd_DD     g_DD  vg_DD  Wg_DD   icc icc_type
##      <dbl>    <dbl>    <dbl>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl> <dbl> <chr>   
## 1   8.61    -4.04    0.9144  0.1139 0.1032  0.8967  0.1135 0.1032   0.1 Imputed 
## 2   1.330   -1.57    0.7078  0.1096 0.1032  0.6941  0.1094 0.1032   0.1 Imputed 
## 3  -0.6100  -0.42   -0.08910 0.1033 0.1032 -0.08738 0.1033 0.1032   0.1 Imputed 
## 4   0.5     -1.620   0.7370  0.1101 0.1032  0.7227  0.1099 0.1032   0.1 Imputed 
## 5  -1.050    0.9000 -0.6828  0.1092 0.1032 -0.6696  0.1089 0.1032   0.1 Imputed 
## 6  -0.2700  -2.520   0.6088  0.1079 0.1032  0.5970  0.1077 0.1032   0.1 Imputed 
##   avg_cl_size avg_cl_type n_covariates gamma_sqrt df_adj  omega gt_post vgt_post
##         <dbl> <chr>              <dbl>      <dbl>  <dbl>  <dbl>   <dbl>    <dbl>
## 1           8 Imputed                1      0.963  36.12 0.9791 -0.2004   0.1379
## 2           8 Imputed                1      0.963  36.12 0.9791  0.2877   0.1385
## 3           8 Imputed                1      0.963  36.12 0.9791  0.1946   0.1378
## 4           8 Imputed                1      0.963  36.12 0.9791  0.5736   0.1419
## 5           8 Imputed                1      0.963  36.12 0.9791  0        0.1373
## 6           8 Imputed                1      0.963  36.12 0.9791  1.242    0.1587
##   Wgt_post    gt_DD vgt_DD Wgt_DD    hg_DD  vhg_DD  h_DD df_DD n_covariates_DD
##      <dbl>    <dbl>  <dbl>  <dbl>    <dbl>   <dbl> <dbl> <dbl>           <dbl>
## 1   0.1373  0.8622  0.1476 0.1373  0.3824  0.02769 36.12 36.12               1
## 2   0.1373  0.6674  0.1435 0.1373  0.2975  0.02769 36.12 36.12               1
## 3   0.1373 -0.08401 0.1374 0.1373 -0.03772 0.02769 36.12 36.12               1
## 4   0.1373  0.6949  0.1440 0.1373  0.3095  0.02769 36.12 36.12               1
## 5   0.1373 -0.6438  0.1431 0.1373 -0.2871  0.02769 36.12 36.12               1
## 6   0.1373  0.5740  0.1419 0.1373  0.2563  0.02769 36.12 36.12               1
##   adj_fct_DD adj_value_DD vary_id                 
##   <chr>             <dbl> <fct>                   
## 1 eta               1.331 Social Functioning (QLS)
## 2 eta               1.331 PANSS Negative          
## 3 eta               1.331 PANSS Excitement        
## 4 eta               1.331 PANSS Cognitive         
## 5 eta               1.331 PANSS Positive          
## 6 eta               1.331 PANSS Depressive

Lloyd-Evans et al. (2020) (Quality-checked)

Entering data from Table 2 (p. 9)

# Dataextraction from Lloyd-Evans (2020)
# Attempt from Jakob

# Loading the relevant package 
library(estmeansd)

# From Table 2 on page 9

# Creating a tibble containing data with IQR, and later i will estimate the mean 
# and SD from this information using bc.mean.sd (Box-Cox method) 

lloyd_Evans2020_IQR <- tibble(
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,5)),
  
  outcome = as.factor(rep(c(
                            "DJG-total",
                            #"DJG-social", removed
                            #"DJG-Emotional", 
                            "GAD-7", 
                            "WEMWBS", 
                            "LSNS6", 
                            "RGUK" 
                            #"ReQoL-10", 
                            #"EQ-VAS", 
                            #"days in acute", removed
                            #"inpatient bed days", removed
                            # "kept appointments"
                            #"missed appointments" removed
                            
# DJG-social, days-in-acute, inpatient bed days, missed appointments: are all 
# removed because there was not enough variance between the IQR to get a an estimate
# Their numbers are therefore also removed further down. 
                            
                            
                          ),
                          each = 2,1)
  ),
  
  N_start = rep(c(30,10),
          each = 1,5),
  
  q1_pre = c(
    10, 9,   #  Loneliness: De Jong-Gierveld (DJG) Loneliness Scale
    # 5, 4,
    # 5, 5,    # DJG: Emotional Loneliness subscale score
    15, 11,  # Generalized Anxiety Disorder Questionnaire (GAD-7) 
    20, 24,  # Warwick-Edinburgh Mental Well-being Scale (WEMWBS) 
    4, 9,    # Social network: Lubben Social Network scale (LSNS6)
    5, 8.8  # Perceived social capital: Resource Generator UK (RGUK)
    # 4, 5,    # Recovering Quality of Life Questionnaire (ReQoL-10)
    # 29, 30,  # Self-rated health using EQ-Visual Analogue Scale (EQ VAS)
    # 0, 0,
    # 0, 0,
    # 3, 1     # Community service kept appointments
    # 0, 0
  ), 
  
  median_pre = c(
    11, 10.5,
    # 5, 5,
    # 6, 6,
    19, 16,
    26.5, 30,
    7, 11.5,
    9.5, 13
   # 9, 9.5
   # 35, 47.5,
   # 0, 0,
   # 0, 0,
   # 6.5, 6.5
   # 0, 0
  ),
  
  q3_pre = c(
    11, 11,
   # 5, 5,
   # 6, 6,
    21, 18,
    32, 34,
    9, 15,
    12, 18.3
   # 14, 15,
   # 50, 50, 
   # 0,0,
   # 0,0,
   # 11, 17
   # 1, 1
  ),
  
  N = rep(c(25,10), # TOT N
               each = 1,5),
  
  q1_post = c(
    8, 7,
    # 4, 4,
    # 4, 4,
    10.5, 11,
    23, 23,
    6, 6,
    6, 6.5
    # 8, 10,
    # 30, 35,
    # 0,0,
    # 0,0,
    # 2, 1
    # 0,0
  ),

  median_post = c(
    9, 10,
    # 5, 4,
    # 5, 6,
    14, 13.5,
    29.5, 31,
    7.5, 11,
    9, 13
    # 14.5, 13.5,
    # 40, 52.5,
    # 0,0,
    # 0,0,
    # 3.5, 8
    # 0, 1.5
    
  ),
  
  q3_post = c(
    11, 11,
    # 5, 5, 
    # 6, 6,
    17.5, 16, 
    34.5, 37,
    11, 15,
    12.3, 22.3
    # 19, 19,
    # 60, 60,
    # 0,0,
    # 0,0,
    # 10, 10
    # 1, 3
  )
) |> 
  rowwise() |> 
  mutate (
    
  pre = list(bc.mean.sd(
    q1.val = q1_pre,
    med.val = median_pre,
    q3.val = q3_pre,
    n = N_start,
    avoid.mc = T
  )[c("est.mean", "est.sd")]),
  
  post = list(bc.mean.sd(
    q1.val = q1_post,
    med.val = median_post,
    q3.val = q3_post,
    n = N,
    avoid.mc = T
  )[c("est.mean", "est.sd")])
  
  ) |> 
  unnest_wider(c(pre, post), names_sep = "_") |> 
  rename(
    
    m_pre = pre_est.mean,
    sd_pre = pre_est.sd,
    m_post = post_est.mean,
    sd_post =  post_est.sd
  
  ) |> 
  suppressWarnings(); lloyd_Evans2020_IQR
## # A tibble: 10 × 14
##    group        outcome   N_start q1_pre median_pre q3_pre     N q1_post
##    <fct>        <fct>       <dbl>  <dbl>      <dbl>  <dbl> <dbl>   <dbl>
##  1 Intervention DJG-total      30   10         11     11      25     8  
##  2 Control      DJG-total      10    9         10.5   11      10     7  
##  3 Intervention GAD-7          30   15         19     21      25    10.5
##  4 Control      GAD-7          10   11         16     18      10    11  
##  5 Intervention WEMWBS         30   20         26.5   32      25    23  
##  6 Control      WEMWBS         10   24         30     34      10    23  
##  7 Intervention LSNS6          30    4          7      9      25     6  
##  8 Control      LSNS6          10    9         11.5   15      10     6  
##  9 Intervention RGUK           30    5          9.5   12      25     6  
## 10 Control      RGUK           10    8.8       13     18.3    10     6.5
##    median_post q3_post  m_pre sd_pre m_post sd_post
##          <dbl>   <dbl>  <dbl>  <dbl>  <dbl>   <dbl>
##  1         9      11   10.61  0.7817  9.566   2.433
##  2        10      11    9.927 1.743   9.140   2.137
##  3        14      17.5 17.90  3.840  14.00    5.276
##  4        13.5    16   14.76  3.499  13.50    4.261
##  5        29.5    34.5 25.58  7.999  28.29    7.396
##  6        31      37   28.59  6.434  29.57    8.435
##  7         7.5    11    6.649 2.112   8.896   4.491
##  8        11      15   12.76  5.888  10.54    4.230
##  9         9      12.3  9.008 2.519   9.349   4.670
## 10        13      22.3 14.62  8.730  16.62   13.99
# Creating a tibble with the rest of the data which is measured by mean and SD
lloyd_Evans2020_means <- tibble(          
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,1)),
  
  outcome = as.factor(rep(c("PHQ-9"
                           #  "TBD",
                           # "EQ-5D-5L
                          ),
  each = 2,1)
  ),
  
  N_start = rep(c(30,10),
          each = 1,1),
  
  m_pre = c(
    21.6, 21.1   # Depression: Patient Health Questionnaire (PHQ-9)
  #  32.7, 38.4,  # Activity: Time Budget Diary (TBD)
  #  0.283, 0.400 # EuroQol Health Questionnaire (EQ-5D-5L) Index value
  ),
  
  sd_pre = c(
    5.3, 4.5
   # 9.6, 11.2,
   # 0.40, 0.24
  ),
  
  N = rep(c(25,10), # TOT N
               each = 1,1),
  
  m_post = c(
    16.4, 18.8
    # 36.9, 35,
    # 0.472, 0.453
  ),
  
  sd_post = c(
    6.8, 4.8
   # 12.9, 14.4,
   # 0.33, 0.236
    
  )
); lloyd_Evans2020_means
## # A tibble: 2 × 8
##   group        outcome N_start m_pre sd_pre     N m_post sd_post
##   <fct>        <fct>     <dbl> <dbl>  <dbl> <dbl>  <dbl>   <dbl>
## 1 Intervention PHQ-9        30  21.6    5.3    25   16.4     6.8
## 2 Control      PHQ-9        10  21.1    4.5    10   18.8     4.8
# Combing the obejct so i have the all the data from table 2 in one obejct
lloyd_Evans2020 <- lloyd_Evans2020_IQR |> 
  full_join(lloyd_Evans2020_means); lloyd_Evans2020
## # A tibble: 12 × 14
##    group        outcome   N_start q1_pre median_pre q3_pre     N q1_post
##    <fct>        <fct>       <dbl>  <dbl>      <dbl>  <dbl> <dbl>   <dbl>
##  1 Intervention DJG-total      30   10         11     11      25     8  
##  2 Control      DJG-total      10    9         10.5   11      10     7  
##  3 Intervention GAD-7          30   15         19     21      25    10.5
##  4 Control      GAD-7          10   11         16     18      10    11  
##  5 Intervention WEMWBS         30   20         26.5   32      25    23  
##  6 Control      WEMWBS         10   24         30     34      10    23  
##  7 Intervention LSNS6          30    4          7      9      25     6  
##  8 Control      LSNS6          10    9         11.5   15      10     6  
##  9 Intervention RGUK           30    5          9.5   12      25     6  
## 10 Control      RGUK           10    8.8       13     18.3    10     6.5
## 11 Intervention PHQ-9          30   NA         NA     NA      25    NA  
## 12 Control      PHQ-9          10   NA         NA     NA      10    NA  
##    median_post q3_post  m_pre sd_pre m_post sd_post
##          <dbl>   <dbl>  <dbl>  <dbl>  <dbl>   <dbl>
##  1         9      11   10.61  0.7817  9.566   2.433
##  2        10      11    9.927 1.743   9.140   2.137
##  3        14      17.5 17.90  3.840  14.00    5.276
##  4        13.5    16   14.76  3.499  13.50    4.261
##  5        29.5    34.5 25.58  7.999  28.29    7.396
##  6        31      37   28.59  6.434  29.57    8.435
##  7         7.5    11    6.649 2.112   8.896   4.491
##  8        11      15   12.76  5.888  10.54    4.230
##  9         9      12.3  9.008 2.519   9.349   4.670
## 10        13      22.3 14.62  8.730  16.62   13.99 
## 11        NA      NA   21.6   5.3    16.4     6.8  
## 12        NA      NA   21.1   4.5    18.8     4.8
# Turning dataset into wide format
lloyd_Evans2020_wide <-
  lloyd_Evans2020 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", 
    "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  )

# Effect size calculating 
lloyd_Evans2020_est <-           
  lloyd_Evans2020_wide |>
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "PHQ-9") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "GAD-7") ~ "All mental health outcomes/Anxiety",
      str_detect(outcome, "DJG-total") ~ "Loneliness",
      str_detect(outcome, "WEMWBS") ~ "Wellbeing and Quality of Life",
      str_detect(outcome, "LSNS6|RGUK") ~ "Unused outcomes",
      .default = NA_character_
    ),
    study = "Lloyd Evans et al. 2020",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor is imputed
     ppcor = ppcor_imp,
     ppcor_method = "Imputed"
  ) |> 
  mutate(
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Reverting m_post for specific outcomes
    m_post = if_else(outcome %in% c("DJG-total", "GAD-7", "PHQ-9"),
                     (m_post_t - m_post_c) * -1, 
                     m_post_t - m_post_c),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # Reverting specific values
   m_diff_t = if_else(outcome %in% c("DJG-total", "GAD-7", "PHQ-9"),
                       (m_post_t - m_pre_t)*-1,
                        m_post_t - m_pre_t),
    m_diff_c = if_else(outcome %in% c("DJG-total", "GAD-7", "PHQ-9"),
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD,
    
    vary_id = outcome
    
  ) |> 
  rowwise() |> 
  mutate(
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "9"),
      omega * ((m_diff_t - m_diff_c)/PHQ_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*PHQ_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
    ) |> 
  ungroup(); lloyd_Evans2020_est
## # A tibble: 6 × 76
##   outcome   N_start_t N_start_c q1_pre_t q1_pre_c median_pre_t median_pre_c
##   <fct>         <dbl>     <dbl>    <dbl>    <dbl>        <dbl>        <dbl>
## 1 DJG-total        30        10       10      9           11           10.5
## 2 GAD-7            30        10       15     11           19           16  
## 3 WEMWBS           30        10       20     24           26.5         30  
## 4 LSNS6            30        10        4      9            7           11.5
## 5 RGUK             30        10        5      8.8          9.5         13  
## 6 PHQ-9            30        10       NA     NA           NA           NA  
##   q3_pre_t q3_pre_c   N_t   N_c q1_post_t q1_post_c median_post_t median_post_c
##      <dbl>    <dbl> <dbl> <dbl>     <dbl>     <dbl>         <dbl>         <dbl>
## 1       11     11      25    10       8         7             9            10  
## 2       21     18      25    10      10.5      11            14            13.5
## 3       32     34      25    10      23        23            29.5          31  
## 4        9     15      25    10       6         6             7.5          11  
## 5       12     18.3    25    10       6         6.5           9            13  
## 6       NA     NA      25    10      NA        NA            NA            NA  
##   q3_post_t q3_post_c m_pre_t m_pre_c sd_pre_t sd_pre_c m_post_t m_post_c
##       <dbl>     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
## 1      11        11    10.61    9.927   0.7817    1.743    9.566    9.140
## 2      17.5      16    17.90   14.76    3.840     3.499   14.00    13.50 
## 3      34.5      37    25.58   28.59    7.999     6.434   28.29    29.57 
## 4      11        15     6.649  12.76    2.112     5.888    8.896   10.54 
## 5      12.3      22.3   9.008  14.62    2.519     8.730    9.349   16.62 
## 6      NA        NA    21.6    21.1     5.3       4.5     16.4     18.8  
##   sd_post_t sd_post_c analysis_plan                        
##       <dbl>     <dbl> <chr>                                
## 1     2.433     2.137 Loneliness                           
## 2     5.276     4.261 All mental health outcomes/Anxiety   
## 3     7.396     8.435 Wellbeing and Quality of Life        
## 4     4.491     4.230 Unused outcomes                      
## 5     4.670    13.99  Unused outcomes                      
## 6     6.8       4.8   All mental health outcomes/Depression
##   study                   effect_size sd_used            main_es_method    ppcor
##   <chr>                   <chr>       <chr>              <chr>             <dbl>
## 1 Lloyd Evans et al. 2020 SMD         Pooled posttest SD Raw diff-in-diffs   0.5
## 2 Lloyd Evans et al. 2020 SMD         Pooled posttest SD Raw diff-in-diffs   0.5
## 3 Lloyd Evans et al. 2020 SMD         Pooled posttest SD Raw diff-in-diffs   0.5
## 4 Lloyd Evans et al. 2020 SMD         Pooled posttest SD Raw diff-in-diffs   0.5
## 5 Lloyd Evans et al. 2020 SMD         Pooled posttest SD Raw diff-in-diffs   0.5
## 6 Lloyd Evans et al. 2020 SMD         Pooled posttest SD Raw diff-in-diffs   0.5
##   ppcor_method N_total df_ind  m_post sd_pool   d_post vd_post Wd_post      J
##   <chr>          <dbl>  <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>
## 1 Imputed           35     35 -0.4265   2.356 -0.1810   0.1405    0.14 0.9784
## 2 Imputed           35     35 -0.5000   5.020 -0.09961  0.1401    0.14 0.9784
## 3 Imputed           35     35 -1.285    7.693 -0.1671   0.1404    0.14 0.9784
## 4 Imputed           35     35 -1.644    4.422 -0.3718   0.1420    0.14 0.9784
## 5 Imputed           35     35 -7.273    8.320 -0.8741   0.1509    0.14 0.9784
## 6 Imputed           35     35  2.400    6.318  0.3799   0.1421    0.14 0.9784
##     g_post vg_post Wg_post m_diff_t m_diff_c    d_DD  vd_DD Wd_DD    g_DD  vg_DD
##      <dbl>   <dbl>   <dbl>    <dbl>    <dbl>   <dbl>  <dbl> <dbl>   <dbl>  <dbl>
## 1 -0.1771   0.1404    0.14   1.042    0.7879  0.1080 0.1402  0.14  0.1056 0.1402
## 2 -0.09746  0.1401    0.14   3.902    1.257   0.5269 0.1440  0.14  0.5155 0.1438
## 3 -0.1635   0.1404    0.14   2.706    0.9768  0.2248 0.1407  0.14  0.2200 0.1407
## 4 -0.3638   0.1419    0.14   2.247   -2.222   1.011  0.1546  0.14  0.9889 0.1540
## 5 -0.8552   0.1504    0.14   0.3402   1.998  -0.1992 0.1406  0.14 -0.1949 0.1405
## 6  0.3717   0.1420    0.14   5.2      2.3     0.4590 0.1430  0.14  0.4491 0.1429
##   Wg_DD vary_id   avg_cl_size avg_cl_type   icc icc_type n_covariates gamma_sqrt
##   <dbl> <fct>           <dbl> <chr>       <dbl> <chr>           <dbl>      <dbl>
## 1  0.14 DJG-total           8 Imputed       0.1 Imputed             1      0.975
## 2  0.14 GAD-7               8 Imputed       0.1 Imputed             1      0.975
## 3  0.14 WEMWBS              8 Imputed       0.1 Imputed             1      0.975
## 4  0.14 LSNS6               8 Imputed       0.1 Imputed             1      0.975
## 5  0.14 RGUK                8 Imputed       0.1 Imputed             1      0.975
## 6  0.14 PHQ-9               8 Imputed       0.1 Imputed             1      0.975
##   df_adj  omega  gt_post vgt_post Wgt_post   gt_DD vgt_DD Wgt_DD    hg_DD
##    <dbl>  <dbl>    <dbl>    <dbl>    <dbl>   <dbl>  <dbl>  <dbl>    <dbl>
## 1  31.65 0.9761 -0.1723    0.1585   0.1581  0.1028 0.1582 0.1581  0.04593
## 2  31.65 0.9761 -0.09480   0.1582   0.1581  0.5015 0.1620 0.1581  0.2233 
## 3  31.65 0.9761 -0.1590    0.1585   0.1581  0.2140 0.1588 0.1581  0.09559
## 4  31.65 0.9761 -0.3538    0.1600   0.1581  0.9619 0.1727 0.1581  0.4237 
## 5  31.65 0.9761 -0.8319    0.1690   0.1581 -0.1896 0.1586 0.1581 -0.08473
## 6  31.65 0.9761  0.3615    0.1601   0.1581  0.4369 0.1611 0.1581  0.1947 
##    vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop
##     <dbl> <dbl> <dbl>           <dbl> <chr>             <dbl>     <dbl>
## 1 0.03160 31.65 31.65               1 eta               1.129   NA     
## 2 0.03160 31.65 31.65               1 eta               1.129   NA     
## 3 0.03160 31.65 31.65               1 eta               1.129   NA     
## 4 0.03160 31.65 31.65               1 eta               1.129   NA     
## 5 0.03160 31.65 31.65               1 eta               1.129   NA     
## 6 0.03160 31.65 31.65               1 eta               1.129    0.4557
##   vgt_DD_pop Wgt_DD_pop
##        <dbl>      <dbl>
## 1    NA         NA     
## 2    NA         NA     
## 3    NA         NA     
## 4    NA         NA     
## 5    NA         NA     
## 6     0.1571     0.1558

Madigan et al. (2013) (Quality-checked)

Entering data from Table 2 (p. 141)

# Table 2, p. 141
Madigan2013 <- tibble(
  group = rep(c("GPI",
                "TAU"),
              each = 1,12),
  
  outcome = rep(c("ASI", 
                 # "BIS",
                # "Attitude to treatment",
                  "Positive symptoms",
                  "Negative symptoms",
                  "Depressive symptoms",
                  "Global functioning (GAF)",
                  "WHOQOL"), 
                each = 2,2),
  
  timing = rep(c("3m", "12m"), each = 12),
  
  N = c(
    # 3 months
    36, 18,
  #  39, 19,
  #  33, 15,
    42, 22,
    40, 20,
    40, 20,
    39, 19,
    34, 15,
    
    # 12 months
    28, 14,
   # 32, 14,
   # 30, 12,
    32, 17,
    32, 19,
    33, 11,
    31, 16,
    34, 14),
  
  N_start = rep(c(
    59, 29,
  # 47, 19,
  # 39, 17,
    59, 28,
    59, 28,
    59, 28,
    58, 29,
    39, 14
  ), each = 1, 2),
 
  m_pre = rep(c(
    10., 10.1,
  #  6.8, 6.3,
  #  5.8, 5.8,
    5.4, 5.7,
    7.7, 7.4,
    5.1, 5.,
    38.3, 38.,
    12.5, 13.3),
    each = 1,2),
  
  
  sd_pre = rep(c(
    3.6, 3.7,
   # 2.8, 2.7,
   # 9.6, 9.7, 
    4., 4.8,
    3.1, 3.,
    5.7, 6.4,
    13.1, 9.,
    4., 2.8),
    each = 1,2),
  
  m_post = c(
    # 3 months
    9.9, 10.1,
  #  7.7, 6.6,
  #  8.1, 5.9,
    4.8, 5.1,
    3.8, 3.2,
    4.4, 4.6,
    37.4, 36.6,
    13.5, 12.6,
    
    #12 months
    9.8, 10.1,
   # 7., 6.6,
   # 7., 6.5,
    4.9, 5.1,
    4.6, 4.8,
    4.3, 4.3,
    37.6, 37.2,
    12.6, 11.1
    ),
  
  
  sd_post = c(
    # 3 months
    4., 4.2,
  #  2.2, 2.4,
  #  9.9, 9.6,
    3.7, 4.1,
    2.4, 2.3,
    4.3, 4.8,
    8., 9.6,
    3.3, 3.4,
    
    #12 months
    3.9, 4.,
  #  2.9, 1.5,
  #  9.5, 9.3,
    4., 4.2,
    3., 3.2,
    4.4, 4.2,
    8.34, 11.5,
    3.4, 2.9)
  
); Madigan2013
## # A tibble: 24 × 9
##    group outcome                  timing     N N_start m_pre sd_pre m_post
##    <chr> <chr>                    <chr>  <dbl>   <dbl> <dbl>  <dbl>  <dbl>
##  1 GPI   ASI                      3m        36      59  10      3.6    9.9
##  2 TAU   ASI                      3m        18      29  10.1    3.7   10.1
##  3 GPI   Positive symptoms        3m        42      59   5.4    4      4.8
##  4 TAU   Positive symptoms        3m        22      28   5.7    4.8    5.1
##  5 GPI   Negative symptoms        3m        40      59   7.7    3.1    3.8
##  6 TAU   Negative symptoms        3m        20      28   7.4    3      3.2
##  7 GPI   Depressive symptoms      3m        40      59   5.1    5.7    4.4
##  8 TAU   Depressive symptoms      3m        20      28   5      6.4    4.6
##  9 GPI   Global functioning (GAF) 3m        39      58  38.3   13.1   37.4
## 10 TAU   Global functioning (GAF) 3m        19      29  38      9     36.6
## 11 GPI   WHOQOL                   3m        34      39  12.5    4     13.5
## 12 TAU   WHOQOL                   3m        15      14  13.3    2.8   12.6
## 13 GPI   ASI                      12m       28      59  10      3.6    9.8
## 14 TAU   ASI                      12m       14      29  10.1    3.7   10.1
## 15 GPI   Positive symptoms        12m       32      59   5.4    4      4.9
## 16 TAU   Positive symptoms        12m       17      28   5.7    4.8    5.1
## 17 GPI   Negative symptoms        12m       32      59   7.7    3.1    4.6
## 18 TAU   Negative symptoms        12m       19      28   7.4    3      4.8
## 19 GPI   Depressive symptoms      12m       33      59   5.1    5.7    4.3
## 20 TAU   Depressive symptoms      12m       11      28   5      6.4    4.3
## 21 GPI   Global functioning (GAF) 12m       31      58  38.3   13.1   37.6
## 22 TAU   Global functioning (GAF) 12m       16      29  38      9     37.2
## 23 GPI   WHOQOL                   12m       34      39  12.5    4     12.6
## 24 TAU   WHOQOL                   12m       14      14  13.3    2.8   11.1
##    sd_post
##      <dbl>
##  1    4   
##  2    4.2 
##  3    3.7 
##  4    4.1 
##  5    2.4 
##  6    2.3 
##  7    4.3 
##  8    4.8 
##  9    8   
## 10    9.6 
## 11    3.3 
## 12    3.4 
## 13    3.9 
## 14    4   
## 15    4   
## 16    4.2 
## 17    3   
## 18    3.2 
## 19    4.4 
## 20    4.2 
## 21    8.34
## 22   11.5 
## 23    3.4 
## 24    2.9
# Turning data into wide format
Madigan2013_wide <- Madigan2013 |>  
      mutate(group = case_match(group, "GPI" ~ "t", "TAU" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col())

# Multi level effect estimates can be obtained from table 3, p. 3
Madigan2013_est <- 
  Madigan2013_wide |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "ASI") ~ "Alcohol and drug abuse/misuse",
      str_detect(outcome, "Depressive symptoms") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "Positive") ~ "All mental health outcomes",
      str_detect(outcome, "Negative") ~ "All mental health outcomes/Negative symptoms",
      str_detect(outcome, "Global") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "WHOQOL") ~ "Wellbeing and Quality of Life",
      .default = NA_character_
    ),
    
    
    study = c("Madigan et al. 2013"),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    
  ) |> 
  mutate(
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
  
   # Reverting m_post for specific outcomes
    m_post = if_else(outcome %in% c("ASI", 
                                    "Positive symptoms", 
                                    "Negative symptoms",
                                    "Depressive symptoms"),
                     (m_post_t - m_post_c) * -1, 
                     m_post_t - m_post_c),
    
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
   
   
   # Reverting specific values
   m_diff_t = if_else(outcome %in% c("ASI", 
                                    "Positive symptoms", 
                                    "Negative symptoms",
                                    "Depressive symptoms"),
                       (m_post_t - m_pre_t)*-1,
                        m_post_t - m_pre_t),
    m_diff_c = if_else(outcome %in% c("ASI", 
                                    "Positive symptoms", 
                                    "Negative symptoms",
                                    "Depressive symptoms"),
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
  
    
  ) |>
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
  
    
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * ((m_diff_t - m_diff_c)/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste0(outcome, "/", timing)
    
  ) |> 
  ungroup(); Madigan2013_est
## # A tibble: 12 × 65
##    outcome                  timing   N_t   N_c N_start_t N_start_c m_pre_t
##    <chr>                    <chr>  <dbl> <dbl>     <dbl>     <dbl>   <dbl>
##  1 ASI                      3m        36    18        59        29    10  
##  2 Positive symptoms        3m        42    22        59        28     5.4
##  3 Negative symptoms        3m        40    20        59        28     7.7
##  4 Depressive symptoms      3m        40    20        59        28     5.1
##  5 Global functioning (GAF) 3m        39    19        58        29    38.3
##  6 WHOQOL                   3m        34    15        39        14    12.5
##  7 ASI                      12m       28    14        59        29    10  
##  8 Positive symptoms        12m       32    17        59        28     5.4
##  9 Negative symptoms        12m       32    19        59        28     7.7
## 10 Depressive symptoms      12m       33    11        59        28     5.1
## 11 Global functioning (GAF) 12m       31    16        58        29    38.3
## 12 WHOQOL                   12m       34    14        39        14    12.5
##    m_pre_c sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##      <dbl>    <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
##  1    10.1      3.6      3.7      9.9     10.1      4          4.2
##  2     5.7      4        4.8      4.8      5.1      3.7        4.1
##  3     7.4      3.1      3        3.8      3.2      2.4        2.3
##  4     5        5.7      6.4      4.4      4.6      4.3        4.8
##  5    38       13.1      9       37.4     36.6      8          9.6
##  6    13.3      4        2.8     13.5     12.6      3.3        3.4
##  7    10.1      3.6      3.7      9.8     10.1      3.9        4  
##  8     5.7      4        4.8      4.9      5.1      4          4.2
##  9     7.4      3.1      3        4.6      4.8      3          3.2
## 10     5        5.7      6.4      4.3      4.3      4.4        4.2
## 11    38       13.1      9       37.6     37.2      8.34      11.5
## 12    13.3      4        2.8     12.6     11.1      3.4        2.9
##    analysis_plan                                study               effect_size
##    <chr>                                        <chr>               <chr>      
##  1 Alcohol and drug abuse/misuse                Madigan et al. 2013 SMD        
##  2 All mental health outcomes                   Madigan et al. 2013 SMD        
##  3 All mental health outcomes/Negative symptoms Madigan et al. 2013 SMD        
##  4 All mental health outcomes/Depression        Madigan et al. 2013 SMD        
##  5 Social functioning (degree of impairment)    Madigan et al. 2013 SMD        
##  6 Wellbeing and Quality of Life                Madigan et al. 2013 SMD        
##  7 Alcohol and drug abuse/misuse                Madigan et al. 2013 SMD        
##  8 All mental health outcomes                   Madigan et al. 2013 SMD        
##  9 All mental health outcomes/Negative symptoms Madigan et al. 2013 SMD        
## 10 All mental health outcomes/Depression        Madigan et al. 2013 SMD        
## 11 Social functioning (degree of impairment)    Madigan et al. 2013 SMD        
## 12 Wellbeing and Quality of Life                Madigan et al. 2013 SMD        
##    sd_used            main_es_method    ppcor ppcor_method N_total df_ind
##    <chr>              <chr>             <dbl> <chr>          <dbl>  <dbl>
##  1 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           54     54
##  2 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           64     64
##  3 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           60     60
##  4 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           60     60
##  5 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           58     58
##  6 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           49     49
##  7 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           42     42
##  8 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           49     49
##  9 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           51     51
## 10 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           44     44
## 11 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           47     47
## 12 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           48     48
##     m_post sd_pool   d_post vd_post Wd_post      J   g_post vg_post Wg_post
##      <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>   <dbl>
##  1  0.2000   4.066  0.04918 0.08336 0.08333 0.9860  0.04850 0.08336 0.08333
##  2  0.3000   3.840  0.07812 0.06931 0.06926 0.9882  0.07720 0.06931 0.06926
##  3 -0.6000   2.368 -0.2534  0.07554 0.075   0.9874 -0.2502  0.07552 0.075  
##  4  0.2000   4.470  0.04474 0.07502 0.075   0.9874  0.04418 0.07502 0.075  
##  5  0.8000   8.547  0.09360 0.07835 0.07827 0.9870  0.09238 0.07835 0.07827
##  6  0.9      3.330  0.2703  0.09682 0.09608 0.9846  0.2661  0.09680 0.09608
##  7  0.3000   3.933  0.07628 0.1072  0.1071  0.9820  0.07491 0.1072  0.1071 
##  8  0.2000   4.069  0.04915 0.09010 0.09007 0.9846  0.04839 0.09010 0.09007
##  9  0.2000   3.075  0.06504 0.08392 0.08388 0.9852  0.06408 0.08392 0.08388
## 10  0        4.353  0       0.1212  0.1212  0.9829  0       0.1212  0.1212 
## 11  0.4000   9.511  0.04206 0.09478 0.09476 0.9840  0.04138 0.09478 0.09476
## 12  1.5      3.266  0.4592  0.1030  0.1008  0.9843  0.4520  0.1030  0.1008 
##    m_diff_t m_diff_c     d_DD   vd_DD   Wd_DD     g_DD   vg_DD   Wg_DD
##       <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
##  1   0.1000   0       0.02459 0.08334 0.08333  0.02425 0.08334 0.08333
##  2   0.6000   0.6000  0       0.06926 0.06926  0       0.06926 0.06926
##  3   3.9      4.2    -0.1267  0.07513 0.075   -0.1251  0.07513 0.075  
##  4   0.7000   0.4000  0.06711 0.07504 0.075    0.06627 0.07504 0.075  
##  5  -0.9000  -1.400   0.05850 0.07830 0.07827  0.05774 0.07830 0.07827
##  6   1       -0.7000  0.5105  0.09874 0.09608  0.5026  0.09866 0.09608
##  7   0.2000   0       0.05085 0.1072  0.1071   0.04994 0.1072  0.1071 
##  8   0.5      0.6000 -0.02457 0.09008 0.09007 -0.02420 0.09008 0.09007
##  9   3.1      2.6     0.1626  0.08414 0.08388  0.1602  0.08413 0.08388
## 10   0.8      0.7     0.02297 0.1212  0.1212   0.02258 0.1212  0.1212 
## 11  -0.7000  -0.8000  0.01051 0.09476 0.09476  0.01035 0.09476 0.09476
## 12   0.1000  -2.20    0.7041  0.1060  0.1008   0.6931  0.1058  0.1008 
##    avg_cl_size avg_cl_type   icc icc_type n_covariates gamma_sqrt df_adj  omega
##          <dbl> <chr>       <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
##  1           8 Imputed       0.1 Imputed             1      0.977  49.8  0.9849
##  2           8 Imputed       0.1 Imputed             1      0.977  59.34 0.9873
##  3           8 Imputed       0.1 Imputed             1      0.977  55.5  0.9864
##  4           8 Imputed       0.1 Imputed             1      0.977  55.5  0.9864
##  5           8 Imputed       0.1 Imputed             1      0.977  53.58 0.9859
##  6           8 Imputed       0.1 Imputed             1      0.977  44.97 0.9832
##  7           8 Imputed       0.1 Imputed             1      0.975  38.4  0.9803
##  8           8 Imputed       0.1 Imputed             1      0.975  45.09 0.9833
##  9           8 Imputed       0.1 Imputed             1      0.974  47.08 0.9840
## 10           8 Imputed       0.1 Imputed             1      0.98   40.08 0.9812
## 11           8 Imputed       0.1 Imputed             1      0.975  43.17 0.9825
## 12           8 Imputed       0.1 Imputed             1      0.978  43.98 0.9828
##     gt_post vgt_post Wgt_post    gt_DD  vgt_DD  Wgt_DD     hg_DD  vhg_DD  h_DD
##       <dbl>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>     <dbl>   <dbl> <dbl>
##  1  0.04732  0.09727  0.09725  0.02366 0.09726 0.09725  0.01075  0.02008 49.8 
##  2  0.07536  0.08143  0.08139  0       0.08139 0.08139  0        0.01685 59.34
##  3 -0.2442   0.08806  0.08753 -0.1221  0.08766 0.08753 -0.05539  0.01802 55.5 
##  4  0.04312  0.08754  0.08753  0.06468 0.08756 0.08753  0.02934  0.01802 55.5 
##  5  0.09016  0.09103  0.09095  0.05635 0.09098 0.09095  0.02553  0.01866 53.58
##  6  0.2596   0.1108   0.1100   0.4904  0.1127  0.1100   0.2196   0.02224 44.97
##  7  0.07291  0.1251   0.1250   0.04861 0.1251  0.1250   0.02218  0.02604 38.4 
##  8  0.04712  0.1061   0.1061  -0.02356 0.1061  0.1061  -0.01077  0.02218 45.09
##  9  0.06234  0.1005   0.1005   0.1558  0.1007  0.1005   0.07162  0.02124 47.08
## 10  0        0.1333   0.1333   0.02209 0.1333  0.1333   0.009555 0.02495 40.08
## 11  0.04029  0.1111   0.1111   0.01007 0.1111  0.1111   0.004600 0.02316 43.17
## 12  0.4414   0.1165   0.1143   0.6768  0.1195  0.1143   0.2997   0.02274 43.98
##    df_DD n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop Wgt_DD_pop
##    <dbl>           <dbl> <chr>             <dbl>     <dbl>      <dbl>      <dbl>
##  1 49.8                1 eta               1.167 NA          NA         NA      
##  2 59.34               1 eta               1.175 NA          NA         NA      
##  3 55.5                1 eta               1.167 NA          NA         NA      
##  4 55.5                1 eta               1.167 NA          NA         NA      
##  5 53.58               1 eta               1.162  0.03782     0.08693    0.08693
##  6 44.97               1 eta               1.145 NA          NA         NA      
##  7 38.4                1 eta               1.167 NA          NA         NA      
##  8 45.09               1 eta               1.178 NA          NA         NA      
##  9 47.08               1 eta               1.198 NA          NA         NA      
## 10 40.08               1 eta               1.1   NA          NA         NA      
## 11 43.17               1 eta               1.172  0.007522    0.1061     0.1061 
## 12 43.98               1 eta               1.133 NA          NA         NA      
##    vary_id                     
##    <chr>                       
##  1 ASI/3m                      
##  2 Positive symptoms/3m        
##  3 Negative symptoms/3m        
##  4 Depressive symptoms/3m      
##  5 Global functioning (GAF)/3m 
##  6 WHOQOL/3m                   
##  7 ASI/12m                     
##  8 Positive symptoms/12m       
##  9 Negative symptoms/12m       
## 10 Depressive symptoms/12m     
## 11 Global functioning (GAF)/12m
## 12 WHOQOL/12m

McCay et al. (2006) (Quality-checked)

Entering data from Table 2 (p. 109)

McCay2006_est <- 
  tibble(
    
    study = "McCay 2006",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    outcome = c("MES", "QLS total", "PANSS Negative", "PANSS total"),
    analysis_plan = c(
      "Self-esteem", "Wellbeing and Quality of Life", 
      "All mental health outcomes/Negative symptoms", "All mental health outcomes/Symptoms of psychosis"
      ),
    
    
    N_t = 26,
    N_c = 14,
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    N_start_t = N_t, 
    N_start_c = N_c,
    
    n_covariates = 1,
    
    m_pre_t = c(78.2, 77.2, 11.7, 26.5),
    sd_pre_t = c(19.8, 14.5, 3.7, 5.3),
    
    m_post_t = c(67.9, 82.5, 10.6, 22.2),
    sd_post_t = c(18.9, 11.6, 3.5, 3.5),
    
    tval_paired_t = c(3.6, -2, 1.6, 4),
    
    r_t =((sd_pre_t^2*tval_paired_t^2 + sd_post_t^2*tval_paired_t^2) - 
           (m_post_t-m_pre_t)^2 * N_t)/ (2 * sd_pre_t*sd_post_t*tval_paired_t^2),
    
    z_t = 0.5 * log( (1+r_t)/(1-r_t) ),
    v_t = 1/(N_t-3),
    w_t = 1/v_t,
    
    m_pre_c = c(69.6, 82.2, 10.6, 24.9),
    sd_pre_c = c(18.9, 13.4, 2.7, 5.3),
    
    m_post_c = c(69.7, 85.9, 10.1, 23.6),
    sd_post_c = c(19.2, 12.1, 3, 6),
    
    tval_paired_c = c(-0.01, -1.5, 0.75, 0.89),
    
    r_c =((sd_pre_c^2*tval_paired_c^2 + sd_post_c^2*tval_paired_c^2) - 
           (m_post_c-m_pre_c)^2 * N_c)/ (2 * sd_pre_c*sd_post_c*tval_paired_c^2)
    
  ) |> 
  mutate(
   
    r_c = if_else(r_c < 0, abs(r_c), r_c),
    
    z_c = 0.5 * log( (1+r_c)/(1-r_c) ),
    v_c = 1/(N_c-3),
    w_c = 1/v_c,
     
    mean_z =  (w_t*z_t + w_c*z_c)/(w_t + w_c),  
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "Calculated from study results",
    
    #  Lower scores on the Modified Engulfment Scale and Positive and Negative Symptoms Scale 
    #indicate less engulfment and fewer symptoms respectively; higher scores on the Quality of 
    # Life Scale indicate improved adjustment
    
    m_post = if_else(str_detect(outcome, "QLS"), (m_post_t - m_post_c), (m_post_t - m_post_c)*-1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For YMRS and HAM-D lower scores are beneficial why these are reverted
    m_diff_t = if_else(str_detect(outcome, "QLS"), (m_post_t - m_pre_t), (m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(str_detect(outcome, "QLS"), (m_post_c - m_pre_c), (m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate( 
    
    # 
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ), 
    
    vary_id = outcome
    
  ) |> 
  ungroup(); McCay2006_est
## # A tibble: 4 × 72
##   study      effect_size sd_used             main_es_method    outcome       
##   <chr>      <chr>       <chr>               <chr>             <chr>         
## 1 McCay 2006 SMD         Pooled posttest SDs Raw diff-in-diffs MES           
## 2 McCay 2006 SMD         Pooled posttest SDs Raw diff-in-diffs QLS total     
## 3 McCay 2006 SMD         Pooled posttest SDs Raw diff-in-diffs PANSS Negative
## 4 McCay 2006 SMD         Pooled posttest SDs Raw diff-in-diffs PANSS total   
##   analysis_plan                                      N_t   N_c N_total df_ind
##   <chr>                                            <dbl> <dbl>   <dbl>  <dbl>
## 1 Self-esteem                                         26    14      40     40
## 2 Wellbeing and Quality of Life                       26    14      40     40
## 3 All mental health outcomes/Negative symptoms        26    14      40     40
## 4 All mental health outcomes/Symptoms of psychosis    26    14      40     40
##   N_start_t N_start_c n_covariates m_pre_t sd_pre_t m_post_t sd_post_t
##       <dbl>     <dbl>        <dbl>   <dbl>    <dbl>    <dbl>     <dbl>
## 1        26        14            1    78.2     19.8     67.9      18.9
## 2        26        14            1    77.2     14.5     82.5      11.6
## 3        26        14            1    11.7      3.7     10.6       3.5
## 4        26        14            1    26.5      5.3     22.2       3.5
##   tval_paired_t    r_t    z_t     v_t   w_t m_pre_c sd_pre_c m_post_c sd_post_c
##           <dbl>  <dbl>  <dbl>   <dbl> <dbl>   <dbl>    <dbl>    <dbl>     <dbl>
## 1           3.6 0.7167 0.9008 0.04348    23    69.6     18.9     69.7      19.2
## 2          -2   0.4822 0.5259 0.04348    23    82.2     13.4     85.9      12.1
## 3           1.6 0.5271 0.5861 0.04348    23    10.6      2.7     10.1       3  
## 4           4   0.2775 0.2849 0.04348    23    24.9      5.3     23.6       6  
##   tval_paired_c    r_c    z_c     v_c   w_c mean_z  ppcor
##           <dbl>  <dbl>  <dbl>   <dbl> <dbl>  <dbl>  <dbl>
## 1         -0.01 0.9289 1.650  0.09091    11 1.143  0.8155
## 2         -1.5  0.7425 0.9561 0.09091    11 0.6651 0.5817
## 3          0.75 0.6215 0.7274 0.09091    11 0.6318 0.5593
## 4          0.89 0.5381 0.6014 0.09091    11 0.3873 0.3690
##   ppcor_method                  m_post sd_pool   d_post vd_post Wd_post      J
##   <chr>                          <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>
## 1 Calculated from study results  1.800  19.00   0.09472  0.1100  0.1099 0.9811
## 2 Calculated from study results -3.400  11.77  -0.2888   0.1109  0.1099 0.9811
## 3 Calculated from study results -0.5     3.337 -0.1498   0.1102  0.1099 0.9811
## 4 Calculated from study results  1.400   4.514  0.3102   0.1111  0.1099 0.9811
##     g_post vg_post Wg_post m_diff_t m_diff_c   d_DD   vd_DD   Wd_DD   g_DD
##      <dbl>   <dbl>   <dbl>    <dbl>    <dbl>  <dbl>   <dbl>   <dbl>  <dbl>
## 1  0.09293  0.1100  0.1099     10.3  -0.1000 0.5473 0.04429 0.04055 0.5370
## 2 -0.2833   0.1109  0.1099      5.3   3.700  0.1359 0.09216 0.09193 0.1333
## 3 -0.1470   0.1102  0.1099      1.1   0.5    0.1798 0.09726 0.09686 0.1764
## 4  0.3043   0.1110  0.1099      4.3   1.300  0.6646 0.1442  0.1387  0.6521
##     vg_DD   Wg_DD avg_cl_size avg_cl_type   icc icc_type gamma_sqrt df_adj
##     <dbl>   <dbl>       <dbl> <chr>       <dbl> <chr>         <dbl>  <dbl>
## 1 0.04415 0.04055           8 Imputed       0.1 Imputed       0.973  36.55
## 2 0.09215 0.09193           8 Imputed       0.1 Imputed       0.973  36.55
## 3 0.09725 0.09686           8 Imputed       0.1 Imputed       0.973  36.55
## 4 0.1440  0.1387            8 Imputed       0.1 Imputed       0.973  36.55
##    omega  gt_post vgt_post Wgt_post  gt_DD  vgt_DD  Wgt_DD   hg_DD  vhg_DD  h_DD
##    <dbl>    <dbl>    <dbl>    <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl>
## 1 0.9793  0.09026   0.1298   0.1297 0.5215 0.05156 0.04784 0.3894  0.02736 36.55
## 2 0.9793 -0.2752    0.1307   0.1297 0.1295 0.1087  0.1085  0.06501 0.02736 36.55
## 3 0.9793 -0.1428    0.1299   0.1297 0.1713 0.1147  0.1143  0.08377 0.02736 36.55
## 4 0.9793  0.2955    0.1309   0.1297 0.6333 0.1691  0.1636  0.2575  0.02736 36.55
##   df_DD n_covariates_DD adj_fct_DD adj_value_DD vary_id       
##   <dbl>           <dbl> <chr>             <dbl> <chr>         
## 1 36.55               1 eta                1.18 MES           
## 2 36.55               1 eta                1.18 QLS total     
## 3 36.55               1 eta                1.18 PANSS Negative
## 4 36.55               1 eta                1.18 PANSS total

McCay et al. (2007) (Quality-checked)

Results are extracted from Table 3 (p. 6) and Table 4 (p. 7). Sample size data is retrieved from Table 1 (p. 6)

McCay2014_est <-
  tibble(
    
    study = "McCay 2007/2014",
    
    effect_size = "SMD",
    sd_used = "Unknown - d was calculated from ANOVA F-val",
    
    outcome = c("MES", "QLS", "MHS"),
    
    N_t = 29,
    N_c = 18,
    
    N_total = N_t + N_c,
    
    N_start_t = 41, # See text on page 3
    N_start_c = 26,
    
    df_ind = N_total,
    n_covariates = 1,
    
    m_pre_t = c(85.99, 61.90, 137.05),
    sd_pre_t = c(19.38, 17.08, 28.83),
    
    m_post_t = c(77.64, 72.76, 143.52),
    sd_post_t = c(20.28, 15.03, 26.17),
    
    tval_paired_t = c(4.12, -6.59, -2.13),
    
    ppcor = ((sd_pre_t^2*tval_paired_t ^2 + sd_post_t^2*tval_paired_t^2) - 
           (m_post_t-m_pre_t)^2 * N_t)/ (2 * sd_pre_t*sd_post_t*tval_paired_t^2),
    
    ppcor_method = "Calcualted from treatment group scores only",
    
    F_val = c(6.5, 11.67, 6.15),
    main_es_method = "Repeated ANOVA (group x time)",
    
    analysis_plan = c(
      "Self-esteem", "Wellbeing and Quality of Life", 
      "Hope, Empowerment & Self-efficacy"
      ),
    
    d_adj = sqrt((F_val * N_total)/(N_t*N_c)) * sqrt(1-ppcor^2),
    vd_adj = (1/N_t + 1/N_c) * (1-ppcor^2) + d_adj^2/(2*N_total),
    Wd_adj = (1/N_t + 1/N_c) * (1-ppcor^2),
    
    J = 1 - 3/(4*df_ind-1),
    g_adj = J * d_adj,
    vg_adj = (1/N_t + 1/N_c) * (1-ppcor^2) + g_adj^2/(2*N_total),
    Wg_adj = (1/N_t + 1/N_c) * (1-ppcor^2)
    
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # "was 7.0±2.1 (range of three to 12)" (p. 57)
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_adj = omega * d_adj * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_adj, 
      model = "ANCOVA",
      cluster_adj = FALSE,
      R2 = ppcor,
      add_name_to_vars = "_adj",
      vars = -var_term1_adj
    ), 
    vary_id = outcome
    
  ) |> 
  ungroup(); McCay2014_est 
## # A tibble: 3 × 45
##   study           effect_size sd_used                                    
##   <chr>           <chr>       <chr>                                      
## 1 McCay 2007/2014 SMD         Unknown - d was calculated from ANOVA F-val
## 2 McCay 2007/2014 SMD         Unknown - d was calculated from ANOVA F-val
## 3 McCay 2007/2014 SMD         Unknown - d was calculated from ANOVA F-val
##   outcome   N_t   N_c N_total N_start_t N_start_c df_ind n_covariates m_pre_t
##   <chr>   <dbl> <dbl>   <dbl>     <dbl>     <dbl>  <dbl>        <dbl>   <dbl>
## 1 MES        29    18      47        41        26     47            1   85.99
## 2 QLS        29    18      47        41        26     47            1   61.9 
## 3 MHS        29    18      47        41        26     47            1  137.0 
##   sd_pre_t m_post_t sd_post_t tval_paired_t  ppcor
##      <dbl>    <dbl>     <dbl>         <dbl>  <dbl>
## 1    19.38    77.64     20.28          4.12 0.8495
## 2    17.08    72.76     15.03         -6.59 0.8548
## 3    28.83   143.5      26.17         -2.13 0.8274
##   ppcor_method                                F_val
##   <chr>                                       <dbl>
## 1 Calcualted from treatment group scores only  6.5 
## 2 Calcualted from treatment group scores only 11.67
## 3 Calcualted from treatment group scores only  6.15
##   main_es_method                analysis_plan                      d_adj  vd_adj
##   <chr>                         <chr>                              <dbl>   <dbl>
## 1 Repeated ANOVA (group x time) Self-esteem                       0.4036 0.02680
## 2 Repeated ANOVA (group x time) Wellbeing and Quality of Life     0.5320 0.02726
## 3 Repeated ANOVA (group x time) Hope, Empowerment & Self-efficacy 0.4180 0.03026
##    Wd_adj      J  g_adj  vg_adj  Wg_adj avg_cl_size avg_cl_type   icc icc_type
##     <dbl>  <dbl>  <dbl>   <dbl>   <dbl>       <dbl> <chr>       <dbl> <chr>   
## 1 0.02506 0.9840 0.3971 0.02674 0.02506           8 Imputed       0.1 Imputed 
## 2 0.02425 0.9840 0.5234 0.02717 0.02425           8 Imputed       0.1 Imputed 
## 3 0.02840 0.9840 0.4112 0.03020 0.02840           8 Imputed       0.1 Imputed 
##   gamma_sqrt df_adj  omega gt_adj vgt_adj Wgt_adj hg_adj vhg_adj h_adj
##        <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl> <dbl>
## 1      0.973   43.3 0.9826 0.3859 0.01806 0.01634 0.4510 0.02309  43.3
## 2      0.973   43.3 0.9826 0.5086 0.01875 0.01577 0.5976 0.02309  43.3
## 3      0.973   43.3 0.9826 0.3996 0.02059 0.01875 0.4366 0.02309  43.3
##   n_covariates_adj adj_fct_adj adj_value_adj vary_id
##              <dbl> <chr>               <dbl> <chr>  
## 1                1 eta                 1.206 MES    
## 2                1 eta                 1.206 QLS    
## 3                1 eta                 1.206 MHS

Michalak et al. (2015) (Quality-checked)

Entering data from Table 2 (p. 957)

Michalak_2015 <- tibble(
  outcome = rep(c(
    "HAM-D",   # Hamilton Depression Rating Scale
    "BDI"      # BDI
  ), each = 3), 
  
  group = rep(c(
    "MBCT", # mindfulness-based cognitive therapy
    "CBASP", # cognitive behavioral analysis system of psychotherapy
    "TAU" # Treatment as usual (control group)
  ), times = 2), 
  
  N = rep(c(36, 35, 35), times = 2), 
  N_start = N,
  
  m_pre = c(
    23.03, 24.71, 23.87,   # Hamilton Depression Rating Scale
    31.31, 30.26, 29.76    # BDI
  ),
  
  sd_pre = c(
    6.27, 6.69, 6.33,
    9.55, 7.52, 7.42
  ),
  
  m_post = c(
    17.86, 14.64, 21.16,     # Hamilton Depression Rating Scale
    22.69, 22.28, 28.34      # BDI
  ), 
  
  sd_post = c(
    10.37, 8.85, 8.16,
    12.11, 12.54, 10.27
  )
)


Michalak2015_wide_DD <- 
  map(c("MBCT", "CBASP"), \(x)
      Michalak_2015 |> 
      filter(group %in% c(x, "TAU")) |> 
      mutate(group = case_match(group, x ~ "t", "TAU" ~ "c")) |> 
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
      ) |> 
        mutate(
          treatment = x,
          N_total = N_t + N_c
        ) |> 
        relocate(treatment)
  ) |> 
  list_rbind() |> 
  mutate(
  
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    analysis_plan = "All mental health outcomes/Depression"
  
  )


Michalak2015_wide_from_paper <- 
  tibble(
    
    effect_size = "SMD",
    sd_used = "Unknown - extracted from report effect sizes",
    main_es_method = "Covariate-adjusted es from multi-level regression",
    
    analysis_plan = rep(c(
      "Wellbeing and Quality of Life",
      "Social functioning (degree of impairment)"
    ), c(8,2)),
    
    
    outcome = paste0(
      rep(c("SF-36", "SASS"), c(8,2)), "/", 
      rep(c("Vitality", "Social func", "Role", "Mental health", "overall"), each = 2)
    ),
    
    treatment = rep(c("MBCT","CBASP"), each = 1,5),
    N_t = rep(c(36,35), each = 1,5), 
    N_c = 35, 
    N_total = N_t + N_c, 
    
    N_start_t = N_t,
    N_start_c = N_c,
    
    b_reg = c(1.12, 1.32, 
             0.27, 0.74,
             0.52, 0.46,
             0.63, 1.25,
             2.43, 1.68),
    se_b_reg = c(0.90, 0.70,
             0.43, 0.44,
             0.26, 0.24,
             0.95, 0.89,
             1.01, 0.93),
    
    t_val = b_reg/se_b_reg,
    
    es_paper = c(0.31,0.36, 
                 0.12, 0.34,
                 0.48, 0.42,
                 0.14, 0.28,
                 0.57, 0.40),
    
    es_paper_type = "Covariate-adjusted ES (d) from a multi-level model",
    
    sd_total = b_reg/es_paper
    
  )


Michalak_2015_dat <- 
  bind_rows(Michalak2015_wide_DD, Michalak2015_wide_from_paper)

ppcor <- ppcor_imp <- 0.5

Michalak_2015_est <- 
  Michalak_2015_dat |> 
  mutate(
    study = "Michalak et al. 2015",
    vary_id = paste0(outcome, "/", treatment),
    
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    df_ind = N_total,
    m_post = if_else(is.na(es_paper), (m_post_t - m_post_c)*-1, NA_real_),
    sd_pool = if_else(
      is.na(es_paper),
      sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
      sqrt( ((N_total-1)/(N_total-2))*sd_total^2 - ((N_t*N_c)/(N_total * (N_total-2)))*b_reg^2 )
    ),
    
    d_post = m_post/sd_pool,
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = if_else(is.na(es_paper), (1/N_t + 1/N_c), NA_real_),
    
    J =  1 - 3/(4*df_ind-1),
    
    g_post =  J * (m_post/sd_pool),
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # DiD (DD) effect sizes for HAM-D og BDI
    
    
    
    #cluster bias adjustment
    
    # For HAM-D lower scores is beneficial, hence these are reverted
    m_diff_t = if_else(
      str_detect(outcome, "HAM|BDI"),
      (m_post_t - m_pre_t) * -1,
      m_post_t - m_pre_t
    ),
    
    m_diff_c = if_else(
      str_detect(outcome, "HAM|BDI"),
      (m_post_c - m_pre_c) * -1,
      m_post_c - m_pre_c
    ), 
    
    d_DD = (m_diff_t - m_diff_c) / sd_pool,
    vd_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + d_DD^2 / (2 * df_ind),
    Wd_DD = if_else(is.na(es_paper), 2 * (1 - ppcor) * (1 / N_t + 1 / N_c), NA_real_),
    
    g_DD = J * d_DD, 
    vg_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + g_DD^2 / (2 * df_ind),
    Wg_DD = Wd_DD,
    
    # Reg adjusted effect sizes for SF-36 and SASS outcomes
    d_reg = b_reg/sd_pool,
    vd_reg = d_reg^2 * (1/t_val^2 + 1/(2*df_ind)),
    Wd_reg = d_reg^2 * (1/t_val^2),
    
    g_reg = J * d_reg,
    vg_reg = g_reg^2 * (1/t_val^2 + 1/(2*df_ind)),
    Wg_reg = Wd_reg
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 177)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,# Baseline scores only
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3 / (4 * df_adj - 1),
    
    # Cluster-adjusting g_post
    gt_post = if_else(is.na(es_paper), omega * d_post * gamma_sqrt, NA_real_),
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c("vgt_post", "Wgt_post")
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -c("var_term1_DD")
    ),
    
    gt_reg = omega * d_reg * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_reg, 
      model = "reg_coef",
      cluster_adj = TRUE,
      t_val = t_val,
      q = n_covariates,
      add_name_to_vars = "_reg",
      vars = -c("var_term1_reg")
    ),
    
    across(c(Wgt_post, Wgt_DD:adj_value_DD), ~ if_else(!str_detect(outcome, "HAM|BDI"), NA, .x)),
    across(c(Wgt_reg:adj_value_reg), ~ if_else(str_detect(outcome, "HAM|BDI"), NA, .x)),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
  ) |> 
  ungroup(); Michalak_2015_est
## # A tibble: 14 × 87
##    treatment outcome               N_t   N_c N_start_t N_start_c m_pre_t m_pre_c
##    <chr>     <chr>               <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>
##  1 MBCT      HAM-D                  36    35        36        35   23.03   23.87
##  2 MBCT      BDI                    36    35        36        35   31.31   29.76
##  3 CBASP     HAM-D                  35    35        35        35   24.71   23.87
##  4 CBASP     BDI                    35    35        35        35   30.26   29.76
##  5 MBCT      SF-36/Vitality         36    35        36        35   NA      NA   
##  6 CBASP     SF-36/Vitality         35    35        35        35   NA      NA   
##  7 MBCT      SF-36/Social func      36    35        36        35   NA      NA   
##  8 CBASP     SF-36/Social func      35    35        35        35   NA      NA   
##  9 MBCT      SF-36/Role             36    35        36        35   NA      NA   
## 10 CBASP     SF-36/Role             35    35        35        35   NA      NA   
## 11 MBCT      SF-36/Mental health    36    35        36        35   NA      NA   
## 12 CBASP     SF-36/Mental health    35    35        35        35   NA      NA   
## 13 MBCT      SASS/overall           36    35        36        35   NA      NA   
## 14 CBASP     SASS/overall           35    35        35        35   NA      NA   
##    sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c N_total effect_size
##       <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl>   <dbl> <chr>      
##  1     6.27     6.33    17.86    21.16     10.37      8.16      71 SMD        
##  2     9.55     7.42    22.69    28.34     12.11     10.27      71 SMD        
##  3     6.69     6.33    14.64    21.16      8.85      8.16      70 SMD        
##  4     7.52     7.42    22.28    28.34     12.54     10.27      70 SMD        
##  5    NA       NA       NA       NA        NA        NA         71 SMD        
##  6    NA       NA       NA       NA        NA        NA         70 SMD        
##  7    NA       NA       NA       NA        NA        NA         71 SMD        
##  8    NA       NA       NA       NA        NA        NA         70 SMD        
##  9    NA       NA       NA       NA        NA        NA         71 SMD        
## 10    NA       NA       NA       NA        NA        NA         70 SMD        
## 11    NA       NA       NA       NA        NA        NA         71 SMD        
## 12    NA       NA       NA       NA        NA        NA         70 SMD        
## 13    NA       NA       NA       NA        NA        NA         71 SMD        
## 14    NA       NA       NA       NA        NA        NA         70 SMD        
##    sd_used                                     
##    <chr>                                       
##  1 Pooled posttest SD                          
##  2 Pooled posttest SD                          
##  3 Pooled posttest SD                          
##  4 Pooled posttest SD                          
##  5 Unknown - extracted from report effect sizes
##  6 Unknown - extracted from report effect sizes
##  7 Unknown - extracted from report effect sizes
##  8 Unknown - extracted from report effect sizes
##  9 Unknown - extracted from report effect sizes
## 10 Unknown - extracted from report effect sizes
## 11 Unknown - extracted from report effect sizes
## 12 Unknown - extracted from report effect sizes
## 13 Unknown - extracted from report effect sizes
## 14 Unknown - extracted from report effect sizes
##    main_es_method                                   
##    <chr>                                            
##  1 Raw diff-in-diffs                                
##  2 Raw diff-in-diffs                                
##  3 Raw diff-in-diffs                                
##  4 Raw diff-in-diffs                                
##  5 Covariate-adjusted es from multi-level regression
##  6 Covariate-adjusted es from multi-level regression
##  7 Covariate-adjusted es from multi-level regression
##  8 Covariate-adjusted es from multi-level regression
##  9 Covariate-adjusted es from multi-level regression
## 10 Covariate-adjusted es from multi-level regression
## 11 Covariate-adjusted es from multi-level regression
## 12 Covariate-adjusted es from multi-level regression
## 13 Covariate-adjusted es from multi-level regression
## 14 Covariate-adjusted es from multi-level regression
##    analysis_plan                             b_reg se_b_reg   t_val es_paper
##    <chr>                                     <dbl>    <dbl>   <dbl>    <dbl>
##  1 All mental health outcomes/Depression     NA       NA    NA         NA   
##  2 All mental health outcomes/Depression     NA       NA    NA         NA   
##  3 All mental health outcomes/Depression     NA       NA    NA         NA   
##  4 All mental health outcomes/Depression     NA       NA    NA         NA   
##  5 Wellbeing and Quality of Life              1.12     0.9   1.244      0.31
##  6 Wellbeing and Quality of Life              1.32     0.7   1.886      0.36
##  7 Wellbeing and Quality of Life              0.27     0.43  0.6279     0.12
##  8 Wellbeing and Quality of Life              0.74     0.44  1.682      0.34
##  9 Wellbeing and Quality of Life              0.52     0.26  2          0.48
## 10 Wellbeing and Quality of Life              0.46     0.24  1.917      0.42
## 11 Wellbeing and Quality of Life              0.63     0.95  0.6632     0.14
## 12 Wellbeing and Quality of Life              1.25     0.89  1.404      0.28
## 13 Social functioning (degree of impairment)  2.43     1.01  2.406      0.57
## 14 Social functioning (degree of impairment)  1.68     0.93  1.806      0.4 
##    es_paper_type                                      sd_total
##    <chr>                                                 <dbl>
##  1 <NA>                                                 NA    
##  2 <NA>                                                 NA    
##  3 <NA>                                                 NA    
##  4 <NA>                                                 NA    
##  5 Covariate-adjusted ES (d) from a multi-level model    3.613
##  6 Covariate-adjusted ES (d) from a multi-level model    3.667
##  7 Covariate-adjusted ES (d) from a multi-level model    2.25 
##  8 Covariate-adjusted ES (d) from a multi-level model    2.176
##  9 Covariate-adjusted ES (d) from a multi-level model    1.083
## 10 Covariate-adjusted ES (d) from a multi-level model    1.095
## 11 Covariate-adjusted ES (d) from a multi-level model    4.5  
## 12 Covariate-adjusted ES (d) from a multi-level model    4.464
## 13 Covariate-adjusted ES (d) from a multi-level model    4.263
## 14 Covariate-adjusted ES (d) from a multi-level model    4.2  
##    study                vary_id                   ppcor ppcor_method df_ind
##    <chr>                <chr>                     <dbl> <chr>         <dbl>
##  1 Michalak et al. 2015 HAM-D/MBCT                  0.5 Imputed          71
##  2 Michalak et al. 2015 BDI/MBCT                    0.5 Imputed          71
##  3 Michalak et al. 2015 HAM-D/CBASP                 0.5 Imputed          70
##  4 Michalak et al. 2015 BDI/CBASP                   0.5 Imputed          70
##  5 Michalak et al. 2015 SF-36/Vitality/MBCT         0.5 Imputed          71
##  6 Michalak et al. 2015 SF-36/Vitality/CBASP        0.5 Imputed          70
##  7 Michalak et al. 2015 SF-36/Social func/MBCT      0.5 Imputed          71
##  8 Michalak et al. 2015 SF-36/Social func/CBASP     0.5 Imputed          70
##  9 Michalak et al. 2015 SF-36/Role/MBCT             0.5 Imputed          71
## 10 Michalak et al. 2015 SF-36/Role/CBASP            0.5 Imputed          70
## 11 Michalak et al. 2015 SF-36/Mental health/MBCT    0.5 Imputed          71
## 12 Michalak et al. 2015 SF-36/Mental health/CBASP   0.5 Imputed          70
## 13 Michalak et al. 2015 SASS/overall/MBCT           0.5 Imputed          71
## 14 Michalak et al. 2015 SASS/overall/CBASP          0.5 Imputed          70
##    m_post sd_pool  d_post  vd_post  Wd_post      J  g_post  vg_post  Wg_post
##     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>  <dbl>   <dbl>    <dbl>    <dbl>
##  1   3.3    9.347  0.3531  0.05723  0.05635 0.9894  0.3493  0.05721  0.05635
##  2   5.65  11.24   0.5026  0.05813  0.05635 0.9894  0.4973  0.05809  0.05635
##  3   6.52   8.512  0.7660  0.06133  0.05714 0.9892  0.7577  0.06124  0.05714
##  4   6.06  11.46   0.5287  0.05914  0.05714 0.9892  0.5230  0.05910  0.05714
##  5  NA      3.594 NA      NA       NA       0.9894 NA      NA       NA      
##  6  NA      3.632 NA      NA       NA       0.9892 NA      NA       NA      
##  7  NA      2.262 NA      NA       NA       0.9894 NA      NA       NA      
##  8  NA      2.160 NA      NA       NA       0.9892 NA      NA       NA      
##  9  NA      1.059 NA      NA       NA       0.9894 NA      NA       NA      
## 10  NA      1.078 NA      NA       NA       0.9892 NA      NA       NA      
## 11  NA      4.521 NA      NA       NA       0.9894 NA      NA       NA      
## 12  NA      4.452 NA      NA       NA       0.9892 NA      NA       NA      
## 13  NA      4.113 NA      NA       NA       0.9894 NA      NA       NA      
## 14  NA      4.144 NA      NA       NA       0.9892 NA      NA       NA      
##    m_diff_t m_diff_c    d_DD    vd_DD    Wd_DD    g_DD    vg_DD    Wg_DD   d_reg
##       <dbl>    <dbl>   <dbl>    <dbl>    <dbl>   <dbl>    <dbl>    <dbl>   <dbl>
##  1     5.17    2.71   0.2632  0.05684  0.05635  0.2604  0.05683  0.05635 NA     
##  2     8.62    1.420  0.6405  0.05924  0.05635  0.6337  0.05918  0.05635 NA     
##  3    10.07    2.71   0.8647  0.06248  0.05714  0.8554  0.06237  0.05714 NA     
##  4     7.98    1.420  0.5724  0.05948  0.05714  0.5662  0.05943  0.05714 NA     
##  5    NA      NA     NA      NA       NA       NA      NA       NA        0.3116
##  6    NA      NA     NA      NA       NA       NA      NA       NA        0.3634
##  7    NA      NA     NA      NA       NA       NA      NA       NA        0.1194
##  8    NA      NA     NA      NA       NA       NA      NA       NA        0.3426
##  9    NA      NA     NA      NA       NA       NA      NA       NA        0.4911
## 10    NA      NA     NA      NA       NA       NA      NA       NA        0.4266
## 11    NA      NA     NA      NA       NA       NA      NA       NA        0.1393
## 12    NA      NA     NA      NA       NA       NA      NA       NA        0.2808
## 13    NA      NA     NA      NA       NA       NA      NA       NA        0.5908
## 14    NA      NA     NA      NA       NA       NA      NA       NA        0.4054
##      vd_reg   Wd_reg   g_reg   vg_reg   Wg_reg avg_cl_size avg_cl_type        
##       <dbl>    <dbl>   <dbl>    <dbl>    <dbl>       <dbl> <chr>              
##  1 NA       NA       NA      NA       NA                 6 From study (p. 177)
##  2 NA       NA       NA      NA       NA                 6 From study (p. 177)
##  3 NA       NA       NA      NA       NA                 6 From study (p. 177)
##  4 NA       NA       NA      NA       NA                 6 From study (p. 177)
##  5  0.06338  0.06270  0.3083  0.06204  0.06270           6 From study (p. 177)
##  6  0.03808  0.03714  0.3595  0.03727  0.03714           6 From study (p. 177)
##  7  0.03623  0.03613  0.1181  0.03547  0.03613           6 From study (p. 177)
##  8  0.04233  0.04149  0.3389  0.04143  0.04149           6 From study (p. 177)
##  9  0.06200  0.06030  0.4859  0.06069  0.06030           6 From study (p. 177)
## 10  0.05084  0.04954  0.4220  0.04975  0.04954           6 From study (p. 177)
## 11  0.04429  0.04415  0.1379  0.04335  0.04415           6 From study (p. 177)
## 12  0.04053  0.03996  0.2778  0.03966  0.03996           6 From study (p. 177)
## 13  0.06275  0.06029  0.5845  0.06143  0.06029           6 From study (p. 177)
## 14  0.05154  0.05036  0.4010  0.05044  0.05036           6 From study (p. 177)
##      icc icc_type n_covariates gamma_sqrt df_adj  omega gt_post vgt_post
##    <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>   <dbl>    <dbl>
##  1   0.1 Imputed             1      0.971  67.17 0.9888  0.3390  0.06825
##  2   0.1 Imputed             1      0.971  67.17 0.9888  0.4826  0.06913
##  3   0.1 Imputed             1      0.971  66.23 0.9886  0.7353  0.07265
##  4   0.1 Imputed             1      0.971  66.23 0.9886  0.5076  0.07052
##  5   0.1 Imputed             1      0.971  67.17 0.9888 NA      NA      
##  6   0.1 Imputed             1      0.971  66.23 0.9886 NA      NA      
##  7   0.1 Imputed             1      0.971  67.17 0.9888 NA      NA      
##  8   0.1 Imputed             1      0.971  66.23 0.9886 NA      NA      
##  9   0.1 Imputed             1      0.971  67.17 0.9888 NA      NA      
## 10   0.1 Imputed             1      0.971  66.23 0.9886 NA      NA      
## 11   0.1 Imputed             1      0.971  67.17 0.9888 NA      NA      
## 12   0.1 Imputed             1      0.971  66.23 0.9886 NA      NA      
## 13   0.1 Imputed             1      0.971  67.17 0.9888 NA      NA      
## 14   0.1 Imputed             1      0.971  66.23 0.9886 NA      NA      
##    Wgt_post   gt_DD   vgt_DD   Wgt_DD   hg_DD   vhg_DD  h_DD df_DD
##       <dbl>   <dbl>    <dbl>    <dbl>   <dbl>    <dbl> <dbl> <dbl>
##  1  0.06739  0.2527  0.06787  0.06739  0.1186  0.01489 67.17 67.17
##  2  0.06739  0.6150  0.07021  0.06739  0.2871  0.01489 67.17 67.17
##  3  0.06857  0.8300  0.07377  0.06857  0.3847  0.01510 66.23 66.23
##  4  0.06857  0.5494  0.07085  0.06857  0.2564  0.01510 66.23 66.23
##  5 NA       NA      NA       NA       NA      NA       NA    NA   
##  6 NA       NA      NA       NA       NA      NA       NA    NA   
##  7 NA       NA      NA       NA       NA      NA       NA    NA   
##  8 NA       NA      NA       NA       NA      NA       NA    NA   
##  9 NA       NA      NA       NA       NA      NA       NA    NA   
## 10 NA       NA      NA       NA       NA      NA       NA    NA   
## 11 NA       NA      NA       NA       NA      NA       NA    NA   
## 12 NA       NA      NA       NA       NA      NA       NA    NA   
## 13 NA       NA      NA       NA       NA      NA       NA    NA   
## 14 NA       NA      NA       NA       NA      NA       NA    NA   
##    n_covariates_DD adj_fct_DD adj_value_DD  gt_reg  vgt_reg  Wgt_reg   hg_reg
##              <dbl> <chr>             <dbl>   <dbl>    <dbl>    <dbl>    <dbl>
##  1               1 eta               1.196 NA      NA       NA       NA      
##  2               1 eta               1.196 NA      NA       NA       NA      
##  3               1 eta               1.2   NA      NA       NA       NA      
##  4               1 eta               1.2   NA      NA       NA       NA      
##  5              NA <NA>             NA      0.2992  0.05517  0.05450  0.1560 
##  6              NA <NA>             NA      0.3489  0.03319  0.03227  0.2375 
##  7              NA <NA>             NA      0.1146  0.03151  0.03141  0.07885
##  8              NA <NA>             NA      0.3289  0.03687  0.03606  0.2120 
##  9              NA <NA>             NA      0.4715  0.05407  0.05242  0.2500 
## 10              NA <NA>             NA      0.4095  0.04432  0.04305  0.2414 
## 11              NA <NA>             NA      0.1338  0.03851  0.03838  0.08328
## 12              NA <NA>             NA      0.2695  0.03528  0.03473  0.1773 
## 13              NA <NA>             NA      0.5672  0.05481  0.05241  0.3000 
## 14              NA <NA>             NA      0.3892  0.04491  0.04377  0.2276 
##     vhg_reg h_reg df_reg n_covariates_reg adj_fct_reg adj_value_reg gt_DD_pop
##       <dbl> <dbl>  <dbl>            <dbl> <chr>               <dbl>     <dbl>
##  1 NA       NA     NA                  NA <NA>               NA       NA     
##  2 NA       NA     NA                  NA <NA>               NA        0.5771
##  3 NA       NA     NA                  NA <NA>               NA       NA     
##  4 NA       NA     NA                  NA <NA>               NA        0.5257
##  5  0.01489 67.17  67.17                1 gamma               0.943   NA     
##  6  0.01510 66.23  66.23                1 gamma               0.943   NA     
##  7  0.01489 67.17  67.17                1 gamma               0.943   NA     
##  8  0.01510 66.23  66.23                1 gamma               0.943   NA     
##  9  0.01489 67.17  67.17                1 gamma               0.943   NA     
## 10  0.01510 66.23  66.23                1 gamma               0.943   NA     
## 11  0.01489 67.17  67.17                1 gamma               0.943   NA     
## 12  0.01510 66.23  66.23                1 gamma               0.943   NA     
## 13  0.01489 67.17  67.17                1 gamma               0.943   NA     
## 14  0.01510 66.23  66.23                1 gamma               0.943   NA     
##    vgt_DD_pop Wgt_DD_pop
##         <dbl>      <dbl>
##  1   NA         NA      
##  2    0.06604    0.06548
##  3   NA         NA      
##  4    0.06709    0.06663
##  5   NA         NA      
##  6   NA         NA      
##  7   NA         NA      
##  8   NA         NA      
##  9   NA         NA      
## 10   NA         NA      
## 11   NA         NA      
## 12   NA         NA      
## 13   NA         NA      
## 14   NA         NA

Morley et al. (2014) (Quality-checked)

Entering data from Table 2 (p. 136)

# Data from table 2 containing means and standard deviation on page 136

Morley2014 <- tibble(
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,4)),
  
  outcome = as.factor(rep(c("BSS",
                            "HADS_depression",
                            "HADS_anxiety",
                            "SES"), each = 2,1
  )),
  
  N = rep(c(
    122, 63 # obtained from Table 1 (p. 135)
  ), each = 1,4),
  
  
   N_start = rep(c(
    122, 63 # obtained from Table 1 (p. 135)
  ), each = 1,4),
  
#   q = 6, # Controlling for Unemployment status, Suicide Ideation, recent cannabis use, 
        # recent heroin use, life time cocaine use, lifetime amphetamine use (p. 134)

  
  m_pre = c(
    9.5, 12.56, # Beck Scale for Suicide Ideation (range 0–38)
    9.3, 9.89,  # HAD Scale Depression (range 0–21)
    12.58, 12.35, # HAD Scale Anxiety (range 0–21)
    61.76, 65.09  # Self-Efficacy Scale (range 0–90)
  ),
  
  sd_pre = c(
    9.61, 8.55,
    6.66, 5.6, 
    6.66, 5.39,
    15.34, 12.55
  ),
  
  m_post = c(
    5.83, 6,
    6.4, 7.1,
    8.83, 9.71,
    65.31, 65.13
  ),
  
  sd_post = c(
    5.58, 6.61,
    5.43, 4.3,
    5.72, 4.3,
    12.49, 9.58
  )
  
  ); Morley2014
## # A tibble: 8 × 8
##   group        outcome             N N_start m_pre sd_pre m_post sd_post
##   <fct>        <fct>           <dbl>   <dbl> <dbl>  <dbl>  <dbl>   <dbl>
## 1 Intervention BSS               122     122  9.5    9.61   5.83    5.58
## 2 Control      BSS                63      63 12.56   8.55   6       6.61
## 3 Intervention HADS_depression   122     122  9.3    6.66   6.4     5.43
## 4 Control      HADS_depression    63      63  9.89   5.6    7.1     4.3 
## 5 Intervention HADS_anxiety      122     122 12.58   6.66   8.83    5.72
## 6 Control      HADS_anxiety       63      63 12.35   5.39   9.71    4.3 
## 7 Intervention SES               122     122 61.76  15.34  65.31   12.49
## 8 Control      SES                63      63 65.09  12.55  65.13    9.58
# Turning dataset into wide format
Morley2014_wide <-
  Morley2014 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", 
    "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


# Effect size calculating 
Morley2014_est <-           
  Morley2014_wide |>
  mutate(
    
     analysis_plan = case_when(
      str_detect(outcome, "BSS") ~ "All mental health outcomes",
      str_detect(outcome, "HADS_depression") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "HADS_anxiety") ~ "All mental health outcomes/Anxiety",
      str_detect(outcome, "SES") ~ "Hope, Empowerment & Self-efficacy",
      .default = NA_character_
    ),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
 
    study = "Morley et al. 2014",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Reverting m_post for specific outcomes
    m_post = if_else(outcome %in% c("BSS", "HADS_depression", "HADs_anxiety"),
            (m_post_t - m_post_c) * -1, 
            m_post_t - m_post_c),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    
    # Reverting specific values
   m_diff_t = if_else(outcome %in% c("BSS", 
                                     "HADS_depression", 
                                     "HADs_anxiety"),
                       (m_post_t - m_pre_t)*-1,
                        m_post_t - m_pre_t),
    m_diff_c = if_else(outcome %in% c("BSS", 
                                     "HADS_depression", 
                                     "HADs_anxiety"),
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
  
    # Group sizes was between 6 to 8
    avg_cl_size = 7, 
    avg_cl_type = "From study (p. 132)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    )
    
  ) |> 
  ungroup() |> 
  mutate(
    vary_id = outcome
  ); Morley2014_est 
## # A tibble: 4 × 61
##   outcome           N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t
##   <fct>           <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>
## 1 BSS               122    63       122        63    9.5    12.56     9.61
## 2 HADS_depression   122    63       122        63    9.3     9.89     6.66
## 3 HADS_anxiety      122    63       122        63   12.58   12.35     6.66
## 4 SES               122    63       122        63   61.76   65.09    15.34
##   sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
## 1     8.55     5.83     6         5.58      6.61
## 2     5.6      6.4      7.1       5.43      4.3 
## 3     5.39     8.83     9.71      5.72      4.3 
## 4    12.55    65.31    65.13     12.49      9.58
##   analysis_plan                         effect_size sd_used           
##   <chr>                                 <chr>       <chr>             
## 1 All mental health outcomes            SMD         Pooled posttest SD
## 2 All mental health outcomes/Depression SMD         Pooled posttest SD
## 3 All mental health outcomes/Anxiety    SMD         Pooled posttest SD
## 4 Hope, Empowerment & Self-efficacy     SMD         Pooled posttest SD
##   main_es_method    ppcor ppcor_method study              N_total df_ind  m_post
##   <chr>             <dbl> <chr>        <chr>                <dbl>  <dbl>   <dbl>
## 1 Raw diff-in-diffs   0.5 Imputed      Morley et al. 2014     185    185  0.17  
## 2 Raw diff-in-diffs   0.5 Imputed      Morley et al. 2014     185    185  0.7000
## 3 Raw diff-in-diffs   0.5 Imputed      Morley et al. 2014     185    185 -0.8800
## 4 Raw diff-in-diffs   0.5 Imputed      Morley et al. 2014     185    185  0.1800
##   sd_pool   d_post vd_post Wd_post      J   g_post vg_post Wg_post m_diff_t
##     <dbl>    <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>   <dbl>    <dbl>
## 1   5.949  0.02858 0.02407 0.02407 0.9959  0.02846 0.02407 0.02407    3.67 
## 2   5.075  0.1379  0.02412 0.02407 0.9959  0.1374  0.02412 0.02407    2.9  
## 3   5.282 -0.1666  0.02414 0.02407 0.9959 -0.1659  0.02414 0.02407   -3.75 
## 4  11.59   0.01554 0.02407 0.02407 0.9959  0.01547 0.02407 0.02407    3.550
##   m_diff_c     d_DD   vd_DD   Wd_DD     g_DD   vg_DD   Wg_DD avg_cl_size
##      <dbl>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>       <dbl>
## 1  6.56    -0.4858  0.02471 0.02407 -0.4838  0.02470 0.02407           7
## 2  2.79     0.02167 0.02407 0.02407  0.02159 0.02407 0.02407           7
## 3 -2.64    -0.2102  0.02419 0.02407 -0.2093  0.02419 0.02407           7
## 4  0.04000  0.3029  0.02432 0.02407  0.3017  0.02432 0.02407           7
##   avg_cl_type           icc icc_type n_covariates gamma_sqrt df_adj  omega
##   <chr>               <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
## 1 From study (p. 132)   0.1 Imputed             1      0.981  175.4 0.9957
## 2 From study (p. 132)   0.1 Imputed             1      0.981  175.4 0.9957
## 3 From study (p. 132)   0.1 Imputed             1      0.981  175.4 0.9957
## 4 From study (p. 132)   0.1 Imputed             1      0.981  175.4 0.9957
##    gt_post vgt_post Wgt_post    gt_DD  vgt_DD  Wgt_DD     hg_DD   vhg_DD  h_DD
##      <dbl>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>     <dbl>    <dbl> <dbl>
## 1  0.02791  0.02739  0.02739 -0.4745  0.02803 0.02739 -0.2156   0.005701 175.4
## 2  0.1347   0.02744  0.02739  0.02117 0.02739 0.02739  0.009658 0.005701 175.4
## 3 -0.1627   0.02747  0.02739 -0.2053  0.02751 0.02739 -0.09358  0.005701 175.4
## 4  0.01518  0.02739  0.02739  0.2959  0.02764 0.02739  0.1348   0.005701 175.4
##   df_DD n_covariates_DD adj_fct_DD adj_value_DD vary_id        
##   <dbl>           <dbl> <chr>             <dbl> <fct>          
## 1 175.4               1 eta               1.138 BSS            
## 2 175.4               1 eta               1.138 HADS_depression
## 3 175.4               1 eta               1.138 HADS_anxiety   
## 4 175.4               1 eta               1.138 SES

Morton et al. (2012) (Quality-checked)

Entering data from Table 3 (p. 538)

# Data from table 3 containing means and standard deviation (p. 538)
Morton2012 <- tibble(
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,4)),
  
  outcome = as.factor(rep(c("BEST_composite",
  #                          "BEST_BPD_thoughts_&_feelings", The subscaled are removed 
  #                                                          we only use total-scale
  #                          "BEST_BPD_negative_behaviors",
  #                          "BEST_BPD_positive_behaviors",
  #                          "DASS_stress",
                            "DASS_anxiety",
  #                          "DASS_depression",
                            "BHS",
 #                            "AAQ-II",
                            "DERS_total"
 #                           "FFMQ_total",
 #                            "ACS_total"
  ),
  each = 2,1)),
  
  N = rep(c(21, 20),
          each = 1,4),
 
  
  m_pre = c(
    44.57, 49.80, # BEST composite
#     28.48, 30.3, # DASS stress
     23.33, 24.20,# DASS anxiety
#    31.52, 33.7, # DASS depression
    14.40, 15.7, # BHS
    # 24.10, 22.55,# AAQ-II
     131.76, 134.42 # DERS total
    # 96.52, 92.15,# FFMQ total
    # 4.86, 4.93   # ACS total
  ),
  
  sd_pre = c(
    11.16, 12.35,
 #   10.04, 10.16,
    9.34, 11.76,
 #    10.86, 8.74,
    4.87, 3.58,
    # 9.29, 8.32,
     25.15, 19.45
    # 23.01, 20.81,
    # 0.74, 0.68
  ),
  
  m_post = c(
    32.76, 47.42,
 #   26.55, 31.57,
    19.67, 26.28,
#    22.67, 31.00,
    9.7, 16.43,
  #  35.3, 23.1,
    113.04, 140.04
  #  108.81, 90.87,
  #  4.35, 5.08
  ), 
  
  sd_post = c(
    12.47, 11.00,
 #   11.58, 9.93,
    11.02, 8.33,
#    14.73, 8.51,
    6.34, 3.69,
  #  10.8, 7.1,
    17.64, 20.88
  #  19.11, 20.67,
  #  0.65, 0.56
  )

); Morton2012
## # A tibble: 8 × 7
##   group        outcome            N  m_pre sd_pre m_post sd_post
##   <fct>        <fct>          <dbl>  <dbl>  <dbl>  <dbl>   <dbl>
## 1 Intervention BEST_composite    21  44.57  11.16  32.76   12.47
## 2 Control      BEST_composite    20  49.8   12.35  47.42   11   
## 3 Intervention DASS_anxiety      21  23.33   9.34  19.67   11.02
## 4 Control      DASS_anxiety      20  24.2   11.76  26.28    8.33
## 5 Intervention BHS               21  14.4    4.87   9.7     6.34
## 6 Control      BHS               20  15.7    3.58  16.43    3.69
## 7 Intervention DERS_total        21 131.8   25.15 113.0    17.64
## 8 Control      DERS_total        20 134.4   19.45 140.0    20.88
Morton2012_wide <-
  Morton2012 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


Morton2012_wide2 <- tibble(
  outcome = c("DASS_stress", 
              "DASS_depression"),
  
  N_t = c(21),
  N_c = c(20),
  
  m_pre_t = c(
    28.48,
    31.52
  ), 
  
  m_pre_c = c(
    30.3, 
    33.70
  ), 
  
  sd_pre_t = c(
    10.04,
    10.86
  ),
  
  sd_pre_c = c(
    10.16,
    8.74
  ),
  
  m_post_t = c(
    26.55,
    22.67
  ),
  
  m_post_c = c(
    31.57,
    31
  ),
  
  sd_post_t = c(
    11.58,
    14.73
  ),
  
  sd_post_c = c(
    9.93, 
    8.51
  )
  
)



Morton2012_est <- 
  bind_rows(
    Morton2012_wide,
    Morton2012_wide2
  ) |> 
  mutate(
    study = "Morton et al. 2012",
   
     analysis_plan = case_when(
      str_detect(outcome, "BEST|DASS") ~ "All mental health outcomes",
      str_detect(outcome, "BHS") ~ "Hope, Empowerment & Self-efficacy",
      str_detect(outcome, "DERS") ~ "All mental health outcomes/Unused outcomes",
      .default = NA_character_
    ),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
  # Entering test-retest reliability measure from text and 
  # Pfohl, B., Blum, N., St. John, D., McCormick, B., Allen, J., & Black, D.W.
  #   (2009). Reliability and validity of the Borderline Evaluation of
  #   Severity over Time (BEST): A self-rated scale tomeasure severity and
  #   change in persons with Borderline Personality Disorder. Journal of
  #   Personality Disorders, 23, 281–293.  

  ppcor = case_when(
    str_detect(outcome, "BEST") ~ 0.62,
    str_detect(outcome, "DASS") ~ 0.81,
    str_detect(outcome, "BHS") ~ 0.69,
    str_detect(outcome, "DERS") ~ 0.88,
    .default = ppcor_imp
    
  ),
  ppcor_method = "Obtained from test-retest reliability measures"
    
  )




Morton2012_est <- 
  Morton2012_est|> 
  mutate(
    N_start_t = N_t,
    N_start_c = N_c,
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # reverting all scores as lower score is beneficial
    m_post =  (m_post_t - m_post_c) * -1,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    #  reverting all scores as lower score is beneficial
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
  
    # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Morton2012_est
## # A tibble: 6 × 61
##   outcome           N_t   N_c m_pre_t m_pre_c sd_pre_t sd_pre_c m_post_t
##   <chr>           <dbl> <dbl>   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>
## 1 BEST_composite     21    20   44.57    49.8    11.16    12.35    32.76
## 2 DASS_anxiety       21    20   23.33    24.2     9.34    11.76    19.67
## 3 BHS                21    20   14.4     15.7     4.87     3.58     9.7 
## 4 DERS_total         21    20  131.8    134.4    25.15    19.45   113.0 
## 5 DASS_stress        21    20   28.48    30.3    10.04    10.16    26.55
## 6 DASS_depression    21    20   31.52    33.7    10.86     8.74    22.67
##   m_post_c sd_post_t sd_post_c study             
##      <dbl>     <dbl>     <dbl> <chr>             
## 1    47.42     12.47     11    Morton et al. 2012
## 2    26.28     11.02      8.33 Morton et al. 2012
## 3    16.43      6.34      3.69 Morton et al. 2012
## 4   140.0      17.64     20.88 Morton et al. 2012
## 5    31.57     11.58      9.93 Morton et al. 2012
## 6    31        14.73      8.51 Morton et al. 2012
##   analysis_plan                              effect_size sd_used           
##   <chr>                                      <chr>       <chr>             
## 1 All mental health outcomes                 SMD         Pooled posttest SD
## 2 All mental health outcomes                 SMD         Pooled posttest SD
## 3 Hope, Empowerment & Self-efficacy          SMD         Pooled posttest SD
## 4 All mental health outcomes/Unused outcomes SMD         Pooled posttest SD
## 5 All mental health outcomes                 SMD         Pooled posttest SD
## 6 All mental health outcomes                 SMD         Pooled posttest SD
##   main_es_method    ppcor ppcor_method                                  
##   <chr>             <dbl> <chr>                                         
## 1 Raw diff-in-diffs  0.62 Obtained from test-retest reliability measures
## 2 Raw diff-in-diffs  0.81 Obtained from test-retest reliability measures
## 3 Raw diff-in-diffs  0.69 Obtained from test-retest reliability measures
## 4 Raw diff-in-diffs  0.88 Obtained from test-retest reliability measures
## 5 Raw diff-in-diffs  0.81 Obtained from test-retest reliability measures
## 6 Raw diff-in-diffs  0.81 Obtained from test-retest reliability measures
##   N_start_t N_start_c N_total df_ind m_post sd_pool d_post vd_post Wd_post
##       <dbl>     <dbl>   <dbl>  <dbl>  <dbl>   <dbl>  <dbl>   <dbl>   <dbl>
## 1        21        20      41     41  14.66  11.78  1.245   0.1165 0.09762
## 2        21        20      41     41   6.61   9.802 0.6743  0.1032 0.09762
## 3        21        20      41     41   6.73   5.220 1.289   0.1179 0.09762
## 4        21        20      41     41  27     19.29  1.400   0.1215 0.09762
## 5        21        20      41     41   5.02  10.81  0.4645  0.1003 0.09762
## 6        21        20      41     41   8.33  12.11  0.6881  0.1034 0.09762
##        J g_post vg_post Wg_post m_diff_t m_diff_c   d_DD   vd_DD   Wd_DD   g_DD
##    <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>  <dbl>   <dbl>   <dbl>  <dbl>
## 1 0.9816 1.222   0.1158 0.09762   11.81     2.380 0.8007 0.08201 0.07419 0.7860
## 2 0.9816 0.6619  0.1030 0.09762    3.660   -2.080 0.5856 0.04128 0.03710 0.5748
## 3 0.9816 1.266   0.1172 0.09762    4.7     -0.730 1.040  0.07372 0.06052 1.021 
## 4 0.9816 1.374   0.1206 0.09762   18.72    -5.620 1.262  0.04285 0.02343 1.239 
## 5 0.9816 0.4559  0.1002 0.09762    1.93    -1.27  0.2961 0.03816 0.03710 0.2906
## 6 0.9816 0.6754  0.1032 0.09762    8.85     2.700 0.5080 0.04024 0.03710 0.4987
##     vg_DD   Wg_DD avg_cl_size avg_cl_type   icc icc_type n_covariates gamma_sqrt
##     <dbl>   <dbl>       <dbl> <chr>       <dbl> <chr>           <dbl>      <dbl>
## 1 0.08172 0.07419           8 Imputed       0.1 Imputed             1      0.966
## 2 0.04112 0.03710           8 Imputed       0.1 Imputed             1      0.966
## 3 0.07324 0.06052           8 Imputed       0.1 Imputed             1      0.966
## 4 0.04214 0.02343           8 Imputed       0.1 Imputed             1      0.966
## 5 0.03813 0.03710           8 Imputed       0.1 Imputed             1      0.966
## 6 0.04013 0.03710           8 Imputed       0.1 Imputed             1      0.966
##   df_adj  omega gt_post vgt_post Wgt_post  gt_DD  vgt_DD  Wgt_DD  hg_DD  vhg_DD
##    <dbl>  <dbl>   <dbl>    <dbl>    <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl>
## 1  37.89 0.9801  1.179    0.1443   0.1259 0.7581 0.1033  0.09571 0.3930 0.02639
## 2  37.89 0.9801  0.6384   0.1313   0.1259 0.5544 0.05191 0.04785 0.4061 0.02639
## 3  37.89 0.9801  1.221    0.1456   0.1259 0.9849 0.09088 0.07808 0.5580 0.02639
## 4  37.89 0.9801  1.325    0.1491   0.1259 1.195  0.04906 0.03022 1.025  0.02639
## 5  37.89 0.9801  0.4398   0.1285   0.1259 0.2803 0.04889 0.04785 0.2074 0.02639
## 6  37.89 0.9801  0.6515   0.1315   0.1259 0.4810 0.05091 0.04785 0.3535 0.02639
##    h_DD df_DD n_covariates_DD adj_fct_DD adj_value_DD vary_id        
##   <dbl> <dbl>           <dbl> <chr>             <dbl> <chr>          
## 1 37.89 37.89               1 eta                1.29 BEST_composite 
## 2 37.89 37.89               1 eta                1.29 DASS_anxiety   
## 3 37.89 37.89               1 eta                1.29 BHS            
## 4 37.89 37.89               1 eta                1.29 DERS_total     
## 5 37.89 37.89               1 eta                1.29 DASS_stress    
## 6 37.89 37.89               1 eta                1.29 DASS_depression
# Regression coefficients extracted from text (pp.537-539)
# Not used for analysis!
morton2012_es <- tibble(
  
  study = "morton2012",
  
  es_method = "Multilevel model",
  
  group = rep(c("treatment", # Treatment is ACT + TAU
                "control", # Control is TAU.
                "interaction"), each = 1,4),
  
  outcome = rep(c(
                  "BEST_composite", 
#                  "BEST_BPD_thoughts_feelings", 
#                  "BEST_BPD_negative_behaviors", 
                  "DASS_anxiety", 
                  "BHS", 
#                  "AAQ-II", 
                  "DERS_total" 
#                  "FFMQ_total",
#                  "ACS_total"
                  ), each = 3,1),
  
  beta = c(-11.52, -1.8, 9.71,  # BEST Composite
#           -6.6, -0.05, 6.54, # BEST BPD thoughts and feelings
#           -1.11, -3.98, 2.87, # BEST BPD negative and behaviors
           -3.92, 3.98, 7.90, # DASS anxiety
           -4.93, 0.62, 5.55, # BHS
#           5.55, 0.37, -9.88, # AAQ-II
           -19.17, 4.77, 23.94 # DERS total
#           12.30, -0.31, -12.62, # FFMQ total
#           -0.54, 0.18, 0.71), # ACS total
),
  
  se = c(2.75, 3.19, 4.21, 
#         1.77, 2.05, 2.72, 
#         1.04, 0.90, 1.38,
         2.24, 2.50, 3.36,
         1.27, 1.38, 1.87,
#         1.87, 2.65, 3.60,
         5.68, 6.30, 8.48
#         3.71, 4.06, 5.50,
#         0.15, 0.16, 0.21),
),
  
  t = c(2.3, -0.57, 2.3, 
#        -3.72, -0.03, 2.41, 
#        -1.06, -4.42, 2.08,
        -1.74, 1.59, 2.35,
        -3.88, 0.45, 2.96,
#        2.96, 0.139, -2.74,
        -3.38, 0.76, 2.82
#        3.31, -0.08, -2.29,
#        -3.63, 1.15, 3.33), # unsure about this
),
  
  p = c(0.000, 0.575, 0.28, 
#        0.001, 0.979, 0.022, 
#        0.296, 0.0, 0.046,
        0.091, 0.121, 0.025,
        0.000, 0.656, 0.006,
#        0.006, 0.891, 0.010,
        0.002, 0.454, 0.008
#        0.002, 0.939, 0.028,
#        0.001, 0.258, 0.002),
),
  
  d = c(0.99,0.15, 0.81, 
#        0.88, 0.01, 0.85, 
#        1.05, 0.29, 0.75,
        0.41, 0.41, 0.83,
        0.91, 0.11, 1.02,
#        1.02, 0.04, 0.99,
        0.78, 0.19, 0.98
#        0.79, 0.02, 0.80,
#        0.89, 0.30, 1.20)
)
  
)

Patterson et al. (2003) (Quality-checked)

Entering data from Table 3 (p. 21)

# Data from Table 3 (p. 21)  containing means, standard deviation. It also contains
# Time X Interaction coefficients with p-values (estimated with ANOVA) 

Patterson2003 <- 
  tibble(
    group = as.factor(rep(c("FAST Intervention", 
                            "Treatment-as-Usual"), 
                          each = 1, 10)),
    
    timing = rep(c("3m", "6m"), each = 10,1),
    
    outcome = as.factor(rep(c(
      #    "UPSA", 
      "PANSS_Positive", 
      "PANSS_Negative", 
      "PANSS_General", 
      "HAM_D", 
      "QWB"), each = 2, 2)),
    
    N = 16,
    
    N_start = 16,
    
    m_pre = rep(c(
      #    31.9, 41.5,   # UPSA
      12.5, 10.4,   # PANSS Positive
      16.9, 10.1,   # PANSS Negative
      25.0, 22.3,   # PANSS General
      7.8, 4.6,     # Ham-D
      0.53, 0.49    # QWB
    ), each = 1,2),
    
    sd_pre = rep(c(
      #    11.8, 8.3,    # UPSA
      5.6, 4.0,       # PANSS Positive
      6.6, 3.2,     # PANSS Negative
      6.2, 3.7,     # PANSS General
      6.1, 2.8,     # Ham-D
      0.08, 0.08    # QWB
    ), each = 1,2),
    
    
    m_post = c(
      #    41.5, 40.3, # UPSA           3 months
      13.0, 14.0, # PANSS Positive 3 months
      13.0, 10.9, # PANSS Negative 3 months
      21.9, 21.5, # PANSS General  3 months
      7.7, 8.0,   # Ham-D          3 months
      0.55, 0.5, # QWB            3 months
      
      #    42.7, 41.6,  # UPSA           6 months
      12.13, 13.1, # PANSS Positive 6 months 
      14.3, 12.4,  # PANSS Negative 6 months
      23.9, 23.9,  # PANSS General  6 months
      7.2, 7.9,    # Ham-D          6 months
      0.51, 0.49   # QWB            6 months
      
    ),
    
    sd_post = c(
      #    9.5, 7.1,     # UPSA           3 months
      6.6, 6.3,     # PANSS Positive 3 months
      5.1, 4.0,       # PANSS Negative 3 months
      4.7, 4.9,     # PANSS General  3 months
      5.2, 5.3,     # Ham-D          3 months
      0.10, 0.07,   # QWB           3 months
      
      
      
      #    9.7, 9.7,     # UPSA           6 months
      4.3, 5.9,     # PANSS Positive 6 months
      6, 4.4,       # PANSS Negative 6 months
      4.6, 4.4,     # PANSS General  6 months
      4.9, 5.0,       # Ham-D          6 months
      0.07, 0.07    # QWB            6 months
    )
  ) 


# Making the patterson2003 tibble wide in order to estimate the effect sizes and
# further analysis. 

Patterson2003_wide  <- 
  Patterson2003 |> 
  mutate(group = case_match(group, "FAST Intervention" ~ "t", "Treatment-as-Usual" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )   

Patterson2003_est <- 
  Patterson2003_wide |>
  # Based on the degrees of freedom value reported in Druss et al. 2018 table 2 and 3
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "PANSS_General|PANSS_Negative|PANSS_Positive") ~ "All mental health outcomes/Symptoms of psychosis",
      str_detect(outcome, "HAM_D") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "QWB") ~ "Wellbeing and Quality of Life",
      .default = NA_character_
    ),
    
    study = "Patterson et al. 2003",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    
  ) |> 
  
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    # For PANSS and HAM-D lower scores are beneficial why these is reverted
    m_post = if_else(analysis_plan %in% c("All mental health outcomes/Symptoms of psychosis",
                                          "All mental health outcomes/Depression"), 
                     (m_post_t - m_post_c)*-1,
                     m_post_t - m_post_c),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    
    #  reverting specific scores as lower score is beneficial
    m_diff_t = if_else(analysis_plan %in% c("All mental health outcomes/Symptoms of psychosis",
                                            "All mental health outcomes/Depression"), 
                       (m_post_t - m_pre_t)*-1,
                       m_post_t - m_pre_t),
    m_diff_c = if_else(analysis_plan %in% c("All mental health outcomes/Symptoms of psychosis",
                                            "All mental health outcomes/Depression"),
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
    
  ) |> 
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    )
    
  ) |> 
  ungroup() |> 
  mutate(
    vary_id = paste0(outcome, "/", timing)
  ); Patterson2003_est
## # A tibble: 10 × 62
##    timing outcome          N_t   N_c N_start_t N_start_c m_pre_t m_pre_c
##    <chr>  <fct>          <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>
##  1 3m     PANSS_Positive    16    16        16        16   12.5    10.4 
##  2 3m     PANSS_Negative    16    16        16        16   16.9    10.1 
##  3 3m     PANSS_General     16    16        16        16   25      22.3 
##  4 3m     HAM_D             16    16        16        16    7.8     4.6 
##  5 3m     QWB               16    16        16        16    0.53    0.49
##  6 6m     PANSS_Positive    16    16        16        16   12.5    10.4 
##  7 6m     PANSS_Negative    16    16        16        16   16.9    10.1 
##  8 6m     PANSS_General     16    16        16        16   25      22.3 
##  9 6m     HAM_D             16    16        16        16    7.8     4.6 
## 10 6m     QWB               16    16        16        16    0.53    0.49
##    sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##       <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
##  1     5.6      4       13       14         6.6       6.3 
##  2     6.6      3.2     13       10.9       5.1       4   
##  3     6.2      3.7     21.9     21.5       4.7       4.9 
##  4     6.1      2.8      7.7      8         5.2       5.3 
##  5     0.08     0.08     0.55     0.5       0.1       0.07
##  6     5.6      4       12.13    13.1       4.3       5.9 
##  7     6.6      3.2     14.3     12.4       6         4.4 
##  8     6.2      3.7     23.9     23.9       4.6       4.4 
##  9     6.1      2.8      7.2      7.9       4.9       5   
## 10     0.08     0.08     0.51     0.49      0.07      0.07
##    analysis_plan                                    study                
##    <chr>                                            <chr>                
##  1 All mental health outcomes/Symptoms of psychosis Patterson et al. 2003
##  2 All mental health outcomes/Symptoms of psychosis Patterson et al. 2003
##  3 All mental health outcomes/Symptoms of psychosis Patterson et al. 2003
##  4 All mental health outcomes/Depression            Patterson et al. 2003
##  5 Wellbeing and Quality of Life                    Patterson et al. 2003
##  6 All mental health outcomes/Symptoms of psychosis Patterson et al. 2003
##  7 All mental health outcomes/Symptoms of psychosis Patterson et al. 2003
##  8 All mental health outcomes/Symptoms of psychosis Patterson et al. 2003
##  9 All mental health outcomes/Depression            Patterson et al. 2003
## 10 Wellbeing and Quality of Life                    Patterson et al. 2003
##    effect_size sd_used            main_es_method    ppcor ppcor_method N_total
##    <chr>       <chr>              <chr>             <dbl> <chr>          <dbl>
##  1 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           32
##  2 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           32
##  3 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           32
##  4 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           32
##  5 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           32
##  6 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           32
##  7 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           32
##  8 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           32
##  9 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           32
## 10 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           32
##    df_ind   m_post sd_pool   d_post vd_post Wd_post      J   g_post vg_post
##     <dbl>    <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>
##  1     32  1       6.452    0.1550   0.1254   0.125 0.9764  0.1513   0.1254
##  2     32 -2.1     4.583   -0.4582   0.1283   0.125 0.9764 -0.4474   0.1281
##  3     32 -0.4000  4.801   -0.08332  0.1251   0.125 0.9764 -0.08135  0.1251
##  4     32  0.3000  5.250    0.05714  0.1251   0.125 0.9764  0.05579  0.1250
##  5     32  0.05000 0.08631  0.5793   0.1302   0.125 0.9764  0.5656   0.1300
##  6     32  0.9700  5.162    0.1879   0.1256   0.125 0.9764  0.1835   0.1255
##  7     32 -1.9     5.261   -0.3611   0.1270   0.125 0.9764 -0.3526   0.1269
##  8     32  0       4.501    0        0.125    0.125 0.9764  0        0.125 
##  9     32  0.7     4.950    0.1414   0.1253   0.125 0.9764  0.1381   0.1253
## 10     32  0.02000 0.07     0.2857   0.1263   0.125 0.9764  0.2790   0.1262
##    Wg_post m_diff_t m_diff_c    d_DD  vd_DD Wd_DD    g_DD  vg_DD Wg_DD
##      <dbl>    <dbl>    <dbl>   <dbl>  <dbl> <dbl>   <dbl>  <dbl> <dbl>
##  1   0.125 -0.5     -3.6      0.4805 0.1286 0.125  0.4691 0.1284 0.125
##  2   0.125  3.9     -0.8000   1.026  0.1414 0.125  1.001  0.1407 0.125
##  3   0.125  3.1      0.8000   0.4791 0.1286 0.125  0.4677 0.1284 0.125
##  4   0.125  0.1000  -3.4      0.6666 0.1319 0.125  0.6509 0.1316 0.125
##  5   0.125  0.02000  0.01000  0.1159 0.1252 0.125  0.1131 0.1252 0.125
##  6   0.125  0.3700  -2.7      0.5947 0.1305 0.125  0.5806 0.1303 0.125
##  7   0.125  2.600   -2.3      0.9314 0.1386 0.125  0.9093 0.1379 0.125
##  8   0.125  1.100   -1.600    0.5999 0.1306 0.125  0.5857 0.1304 0.125
##  9   0.125  0.6000  -3.3      0.7878 0.1347 0.125  0.7692 0.1342 0.125
## 10   0.125 -0.02000  0       -0.2857 0.1263 0.125 -0.2790 0.1262 0.125
##    avg_cl_size avg_cl_type   icc icc_type n_covariates gamma_sqrt df_adj  omega
##          <dbl> <chr>       <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
##  1           8 Imputed       0.1 Imputed             1      0.963   29.3 0.9742
##  2           8 Imputed       0.1 Imputed             1      0.963   29.3 0.9742
##  3           8 Imputed       0.1 Imputed             1      0.963   29.3 0.9742
##  4           8 Imputed       0.1 Imputed             1      0.963   29.3 0.9742
##  5           8 Imputed       0.1 Imputed             1      0.963   29.3 0.9742
##  6           8 Imputed       0.1 Imputed             1      0.963   29.3 0.9742
##  7           8 Imputed       0.1 Imputed             1      0.963   29.3 0.9742
##  8           8 Imputed       0.1 Imputed             1      0.963   29.3 0.9742
##  9           8 Imputed       0.1 Imputed             1      0.963   29.3 0.9742
## 10           8 Imputed       0.1 Imputed             1      0.963   29.3 0.9742
##     gt_post vgt_post Wgt_post   gt_DD vgt_DD Wgt_DD    hg_DD  vhg_DD  h_DD df_DD
##       <dbl>    <dbl>    <dbl>   <dbl>  <dbl>  <dbl>    <dbl>   <dbl> <dbl> <dbl>
##  1  0.1454    0.1629   0.1625  0.4508 0.1660 0.1625  0.2059  0.03413  29.3  29.3
##  2 -0.4299    0.1657   0.1625  0.9621 0.1783 0.1625  0.4341  0.03413  29.3  29.3
##  3 -0.07816   0.1626   0.1625  0.4494 0.1659 0.1625  0.2052  0.03413  29.3  29.3
##  4  0.05361   0.1625   0.1625  0.6254 0.1692 0.1625  0.2847  0.03413  29.3  29.3
##  5  0.5434    0.1675   0.1625  0.1087 0.1627 0.1625  0.04980 0.03413  29.3  29.3
##  6  0.1763    0.1630   0.1625  0.5579 0.1678 0.1625  0.2543  0.03413  29.3  29.3
##  7 -0.3388    0.1645   0.1625  0.8737 0.1755 0.1625  0.3953  0.03413  29.3  29.3
##  8  0         0.1625   0.1625  0.5627 0.1679 0.1625  0.2565  0.03413  29.3  29.3
##  9  0.1327    0.1628   0.1625  0.7391 0.1718 0.1625  0.3356  0.03413  29.3  29.3
## 10  0.2680    0.1637   0.1625 -0.2680 0.1637 0.1625 -0.1227  0.03413  29.3  29.3
##    n_covariates_DD adj_fct_DD adj_value_DD vary_id          
##              <dbl> <chr>             <dbl> <chr>            
##  1               1 eta                 1.3 PANSS_Positive/3m
##  2               1 eta                 1.3 PANSS_Negative/3m
##  3               1 eta                 1.3 PANSS_General/3m 
##  4               1 eta                 1.3 HAM_D/3m         
##  5               1 eta                 1.3 QWB/3m           
##  6               1 eta                 1.3 PANSS_Positive/6m
##  7               1 eta                 1.3 PANSS_Negative/6m
##  8               1 eta                 1.3 PANSS_General/6m 
##  9               1 eta                 1.3 HAM_D/6m         
## 10               1 eta                 1.3 QWB/6m

Popolo et al. (2019) (Quality-checked)

Entering data from Table 2 (p. 351)

# Data from Table 2 containing means and standard deviation + within effect size.
# Table also contains "between group-group post treatment effect size at post-tratement"
# with a coefficient and "Post hoc power" on page 351

Popolo2019 <- 
  tibble(
    group = rep(c("Intervention", 
                  "Control"), 
                each = 1,3),
    
    outcome = rep(c("CORE_OM",
                    "DERS",
                    "TAS"
                   #"Metacognition_self",
                   #"Metacognition_others",
                   #"Metacognition_decentration",
                   #"Metacognition_mastery"
    ), each = 2,1),
    
    
    N = rep(c(8, 10), 3),
    
    N_start = 10, 
    
    m_pre = c(
      12.85, 15.24, # CORE-OM Total
      95, 100.9, # DERS Total
      54.2, 51.5 # TAS Total
      #    4.3, 4.1, # Metacognition Self
      #    2.1, 2.85, # Metacognition Others
      #    0.75, 1.15, # Metacognition Decentration 
      #    2.9, 3.3    # Metacognition Mastery
    ),
    
    sd_pre = c(
      4.6, 6.68,
      16.19, 17.67,
      11.52, 10.99
      #    1.32, 0.97,
      #    0.52, 1.31,
      #    0.75, 1.03,
      #    1.07, 0.67
    ),
    
    m_post = c(
      7.68, 11.78,
      76.4, 97.11,
      43.5, 52.63
      #    5.75, 3.35,
      #    2.9, 3.3,
      #    1.15, 0.7,
      #    4.18, 3.05
    ),
    
    sd_post = c(
      1.94, 4.59,
      26.78, 29.17,
      11.7, 12.59
      #    1.36, 1.27,
      #    1.07, 0.67,
      #    1.03, 0.48,
      #    1.35, 1.23
    ), 
    
    # Within-group effect size baseline – 3-month follow-up
    within_group_d = c( 
      1.14, 0.64,
      0.74, 0.16,
      1.11, 0.10
      #    1.73, 0.56,
      #    0.99, 0.32,
      #    0.59, 0.51,
      #    1.51, 0.20
    ),
    
    # To calculated the pre-posttest correlation, we need the standard deviation of
    # the mean differences (sd_diff). As can be seen from page 351. We can obtain
    # sd_diff from the reported within-group effect sizes as follows
    sd_diff = abs(m_post - m_pre)/within_group_d,
    
    
    # The group individual pre-posttest correlation can be obtained as follows.
    # Note that we are not able to obtained correlations from d_rm = 0. This only 
    # concerns SCL90 pre-posttest effect sizes for the control group. Hereto, 
    # 
    r = (sd_pre^2 + sd_post^2 - sd_diff^2)/(2*sd_pre*sd_post),
    
    # To estiamte the average pre-posttest correlation
    z = 0.5 * log( (1+r)/(1-r) ),
    v = 1/(N-3),
    w = 1/v
    
  ); Popolo2019
## # A tibble: 6 × 14
##   group        outcome     N N_start  m_pre sd_pre m_post sd_post within_group_d
##   <chr>        <chr>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>   <dbl>          <dbl>
## 1 Intervention CORE_OM     8      10  12.85   4.6    7.68    1.94           1.14
## 2 Control      CORE_OM    10      10  15.24   6.68  11.78    4.59           0.64
## 3 Intervention DERS        8      10  95     16.19  76.4    26.78           0.74
## 4 Control      DERS       10      10 100.9   17.67  97.11   29.17           0.16
## 5 Intervention TAS         8      10  54.2   11.52  43.5    11.7            1.11
## 6 Control      TAS        10      10  51.5   10.99  52.63   12.59           0.1 
##   sd_diff      r      z      v     w
##     <dbl>  <dbl>  <dbl>  <dbl> <dbl>
## 1   4.535 0.2441 0.2491 0.2        5
## 2   5.406 0.5946 0.6848 0.1429     7
## 3  25.14  0.4008 0.4245 0.2        5
## 4  23.69  0.5840 0.6685 0.1429     7
## 5   9.640 0.6554 0.7847 0.2        5
## 6  11.30  0.5478 0.6153 0.1429     7
Popolo2019_wide <-
  Popolo2019 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


Popolo2019_est <- 
  Popolo2019_wide |>
  mutate(
    
    analysis_plan =
      c("All mental health outcomes", 
        "All mental health outcomes/Unused outcomes", 
        "All mental health outcomes"),
    
    study = "Popolo et al. 2019",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    mean_z =  (w_t*z_t + w_c*z_c)/(w_t + w_c),  
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "Calculated from study results",
    
    )|> 
  
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # reverting all scores as lower score is beneficial
    m_post =  (m_post_t - m_post_c) * -1,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
    
  ) |>
  rowwise() |> 
  mutate(
    
    # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc, sqrt = TRUE
    ),
    
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    vary_id = outcome
    
  ) |> 
  ungroup() |> 
  rename(
    d_rm_t = within_group_d_t,
    d_rm_c = within_group_d_c
    
  ); Popolo2019_est
## # A tibble: 3 × 74
##   outcome   N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t sd_pre_c
##   <chr>   <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 CORE_OM     8    10        10        10   12.85   15.24     4.6      6.68
## 2 DERS        8    10        10        10   95     100.9     16.19    17.67
## 3 TAS         8    10        10        10   54.2    51.5     11.52    10.99
##   m_post_t m_post_c sd_post_t sd_post_c d_rm_t d_rm_c sd_diff_t sd_diff_c    r_t
##      <dbl>    <dbl>     <dbl>     <dbl>  <dbl>  <dbl>     <dbl>     <dbl>  <dbl>
## 1     7.68    11.78      1.94      4.59   1.14   0.64     4.535     5.406 0.2441
## 2    76.4     97.11     26.78     29.17   0.74   0.16    25.14     23.69  0.4008
## 3    43.5     52.63     11.7      12.59   1.11   0.1      9.640    11.30  0.6554
##      r_c    z_t    z_c   v_t    v_c   w_t   w_c
##    <dbl>  <dbl>  <dbl> <dbl>  <dbl> <dbl> <dbl>
## 1 0.5946 0.2491 0.6848   0.2 0.1429     5     7
## 2 0.5840 0.4245 0.6685   0.2 0.1429     5     7
## 3 0.5478 0.7847 0.6153   0.2 0.1429     5     7
##   analysis_plan                              study              effect_size
##   <chr>                                      <chr>              <chr>      
## 1 All mental health outcomes                 Popolo et al. 2019 SMD        
## 2 All mental health outcomes/Unused outcomes Popolo et al. 2019 SMD        
## 3 All mental health outcomes                 Popolo et al. 2019 SMD        
##   sd_used            main_es_method    mean_z  ppcor
##   <chr>              <chr>              <dbl>  <dbl>
## 1 Pooled posttest SD Raw diff-in-diffs 0.5032 0.4647
## 2 Pooled posttest SD Raw diff-in-diffs 0.5669 0.5130
## 3 Pooled posttest SD Raw diff-in-diffs 0.6859 0.5953
##   ppcor_method                  N_total df_ind m_post sd_pool d_post vd_post
##   <chr>                           <dbl>  <dbl>  <dbl>   <dbl>  <dbl>   <dbl>
## 1 Calculated from study results      18     18   4.1    3.674 1.116   0.2596
## 2 Calculated from study results      18     18  20.71  28.15  0.7357  0.2400
## 3 Calculated from study results      18     18   9.13  12.21  0.7478  0.2405
##   Wd_post      J g_post vg_post Wg_post m_diff_t m_diff_c   d_DD  vd_DD  Wd_DD
##     <dbl>  <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>  <dbl>  <dbl>  <dbl>
## 1   0.225 0.9577 1.069   0.2567   0.225     5.17    3.46  0.4654 0.2469 0.2409
## 2   0.225 0.9577 0.7046  0.2388   0.225    18.6     3.790 0.5261 0.2268 0.2191
## 3   0.225 0.9577 0.7162  0.2392   0.225    10.7    -1.130 0.9690 0.2082 0.1821
##     g_DD  vg_DD  Wg_DD avg_cl_size avg_cl_type   icc icc_type n_covariates
##    <dbl>  <dbl>  <dbl>       <dbl> <chr>       <dbl> <chr>           <dbl>
## 1 0.4458 0.2464 0.2409           8 Imputed       0.1 Imputed             1
## 2 0.5039 0.2262 0.2191           8 Imputed       0.1 Imputed             1
## 3 0.9280 0.2060 0.1821           8 Imputed       0.1 Imputed             1
##   gamma_sqrt df_adj  omega gt_post vgt_post Wgt_post  gt_DD vgt_DD Wgt_DD  hg_DD
##        <dbl>  <dbl>  <dbl>   <dbl>    <dbl>    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1      0.949     16 0.9524  1.009    0.3342   0.3024 0.4207 0.3293 0.3238 0.1843
## 2      0.949     16 0.9524  0.6649   0.3162   0.3024 0.4755 0.3016 0.2945 0.2182
## 3      0.949     16 0.9524  0.6759   0.3167   0.3024 0.8758 0.2687 0.2447 0.4356
##   vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD adj_value_DD vary_id
##    <dbl> <dbl> <dbl>           <dbl> <chr>             <dbl> <chr>  
## 1 0.0625    16    16               1 eta               1.344 CORE_OM
## 2 0.0625    16    16               1 eta               1.344 DERS   
## 3 0.0625    16    16               1 eta               1.344 TAS

Rabenstein et al. (2015) (Quality-checked)

Data was extracted from Table 2 (p.6)

# Based on Table 2, p. 6
Rabenstein2015 <- 
  tibble(
    group = rep(c("Intervention", "Control"), each = 1, 3),
    
    outcome = rep(c(
      #  "Somatization", 
      #  "OCD", 
      #  "Insecurity", 
      #  "Depression",
      #  "Anxiety",
      #  "Aggressiveness",
      #  "Phobic Anxiety",
      #  "Paranoid Thinking",
      #  "Psychoticism",
      "BSI (GSI)", 
      "BDI", 
      "WHOQOL"
      #  "Physical",
      #  "Mental", 
      #  "Social Relations", 
      #  "Environment"
    ), each = 2,1),
    
    
    
    N = rep(c(
      153, 148
    ), each = 1, 3),
    
    
    N_start = rep(c(
      170
    )),
    
    m_pre = c(
      #    8.11, 8.08,
      #    9.85, 12.04,
      #    5.58, 7.23,
      #    9.22, 10.45,
      #    7.82, 9.91,
      #    4.08, 5.45,
      #    5.15, 6.43,
      #    5.83, 7.24,
      #    4.74, 5.76,
      1.23, 1.48,
      23.39, 25.04,
      40.71, 42.87
      #    50.78, 50.35,
      #    38.52, 37.9,
      #    53.31, 47.49,
      #    61.68, 63
    ),
    
    sd_pre = c(
      #    5.51, 5.79,
      #    5.45, 5.30,
      #    4.04, 3.67,
      #    6.08, 5.28,
      #    5.09, 5.20,
      #    3.61, 3.23,
      #    4.92, 4.83,
      #    4.35, 4.09,
      #    3.72, 3.87,
      0.68, 0.66,
      10.92, 9.83,
      22.38, 21.37
      #    19.56, 15.96,
      #    19.55, 16.43,
      #    24.22, 23.85,
      #    18.62, 16.04
    ),
    
    m_post = c(
      #    4.49, 7.15,
      #    7.02, 10.25,
      #    4.06, 6.63,
      #    5.68, 9.87,
      #    4.45, 8.59,
      #    2.74, 4.15,
      #    2.91, 5.59,
      #    4.82, 6.25,
      #    3.12, 4.93,
      0.81, 1.30,
      15.24, 22.08,
      53.21, 46.1
      #    59.43, 52.07,
      #    48.45, 38.49,
      #    58.23, 50.27,
      #    65.83, 65.83
    ),
    
    sd_post = c(
      #    4.46, 5.64,
      #    5.16, 5.63,
      #    3.53, 4.06,
      #    5.18, 5.63,
      #    4.36, 5.40,
      #    2.45, 3.18,
      #    3.67, 4.62,
      #    4.26, 4.51,
      #    3.25, 3.91,
      0.60, 0.67,
      10.78, 9.95,
      22.83, 20.44
      #    21.37, 15.32,
      #    19.94, 17.32,
      #    23.77, 22.13,
      #    18.08, 17.12
    ),
    
    tval_paired = c(
      7.355,3.428,
      8.377, 4.018,
      -6.695, -1.865
    ),
    
    r = ((sd_pre^2*tval_paired^2 + sd_post^2*tval_paired^2) - 
           (m_post-m_pre)^2 * N)/ (2 * sd_pre*sd_post*tval_paired^2),
    
    
    z = 0.5 * log( (1+r)/(1-r) ),
    v = 1/(N-3),
    w = 1/v
    
    
  ); Rabenstein2015
## # A tibble: 6 × 13
##   group        outcome       N N_start m_pre sd_pre m_post sd_post tval_paired
##   <chr>        <chr>     <dbl>   <dbl> <dbl>  <dbl>  <dbl>   <dbl>       <dbl>
## 1 Intervention BSI (GSI)   153     170  1.23   0.68   0.81    0.6        7.355
## 2 Control      BSI (GSI)   148     170  1.48   0.66   1.3     0.67       3.428
## 3 Intervention BDI         153     170 23.39  10.92  15.24   10.78       8.377
## 4 Control      BDI         148     170 25.04   9.83  22.08    9.95       4.018
## 5 Intervention WHOQOL      153     170 40.71  22.38  53.21   22.83      -6.695
## 6 Control      WHOQOL      148     170 42.87  21.37  46.1    20.44      -1.865
##        r      z        v     w
##    <dbl>  <dbl>    <dbl> <dbl>
## 1 0.3964 0.4194 0.006667   150
## 2 0.5387 0.6023 0.006897   145
## 3 0.3850 0.4059 0.006667   150
## 4 0.5895 0.6769 0.006897   145
## 5 0.4783 0.5207 0.006667   150
## 6 0.4928 0.5398 0.006897   145
# Making it a wide format:

Rabenstein2015_wide <-
  Rabenstein2015 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


# Effect size calculation:

Rabenstein2015_est <-           
  Rabenstein2015_wide |>
  mutate(
    analysis_plan =
      c("All mental health outcomes", 
        "All mental health outcomes/Depression",
        "Wellbeing and Quality of Life"),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
   
    study = "Rabenstein et al. 2015",
    
    # There are also some effect sizes in the raw text on p. 7
    # the time X group effect: ANOVA
    #  df1 = c(1), 
    #  df2 = c(138, 187, 169),
    #  F_val = c(25.584, 20.582, 13.252),
    # p_val_F = c(0.001, 0.001, 0.001),
    #  eta_sqrt = c(0.156, 0.002, 0.0073),
    
    # t-test
    # t_val = c(-4.468, -4.531,2.146),
    # p_val_t = c(0.001, 0.001, 0.012),
    #  d = c(0.76, 0.66, 0.55)
    
    main_es_method = "Raw diff-in-diffs",
    
    mean_z =  (w_t*z_t + w_c*z_c)/(w_t + w_c),  
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "Calculated from study results, i.e., paired t-tests",
    
  ) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # The outcome BSI and BDI are reverted because lower score is beneficial
    m_post = if_else(outcome %in% c("GSI","BDI"), 
                     (m_post_c - m_post_t)*-1, # Revert the difference if BSI or BDI
                     m_post_t - m_post_c),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # The outcome BSI and BDI are reverted because lower score is beneficial  
    m_diff_t = if_else(analysis_plan %in% c("GSI","BDI"),
                       (m_post_t - m_pre_t)*-1, m_post_t - m_pre_t),
    m_diff_c = if_else(analysis_plan %in% c("GSI","BDI"),
                       (m_post_c - m_pre_c)*-1,m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc, sqrt = TRUE
    ),
    
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = outcome
  ) |> 
  ungroup(); Rabenstein2015_est
## # A tibble: 3 × 75
##   outcome     N_t   N_c N_start_t N_start_c m_pre_t m_pre_c sd_pre_t sd_pre_c
##   <chr>     <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 BSI (GSI)   153   148       170       170    1.23    1.48     0.68     0.66
## 2 BDI         153   148       170       170   23.39   25.04    10.92     9.83
## 3 WHOQOL      153   148       170       170   40.71   42.87    22.38    21.37
##   m_post_t m_post_c sd_post_t sd_post_c tval_paired_t tval_paired_c    r_t
##      <dbl>    <dbl>     <dbl>     <dbl>         <dbl>         <dbl>  <dbl>
## 1     0.81     1.3       0.6       0.67         7.355         3.428 0.3964
## 2    15.24    22.08     10.78      9.95         8.377         4.018 0.3850
## 3    53.21    46.1      22.83     20.44        -6.695        -1.865 0.4783
##      r_c    z_t    z_c      v_t      v_c   w_t   w_c
##    <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <dbl> <dbl>
## 1 0.5387 0.4194 0.6023 0.006667 0.006897   150   145
## 2 0.5895 0.4059 0.6769 0.006667 0.006897   150   145
## 3 0.4928 0.5207 0.5398 0.006667 0.006897   150   145
##   analysis_plan                         effect_size sd_used           
##   <chr>                                 <chr>       <chr>             
## 1 All mental health outcomes            SMD         Pooled posttest SD
## 2 All mental health outcomes/Depression SMD         Pooled posttest SD
## 3 Wellbeing and Quality of Life         SMD         Pooled posttest SD
##   study                  main_es_method    mean_z  ppcor
##   <chr>                  <chr>              <dbl>  <dbl>
## 1 Rabenstein et al. 2015 Raw diff-in-diffs 0.5093 0.4694
## 2 Rabenstein et al. 2015 Raw diff-in-diffs 0.5391 0.4923
## 3 Rabenstein et al. 2015 Raw diff-in-diffs 0.5301 0.4855
##   ppcor_method                                        N_total df_ind m_post
##   <chr>                                                 <dbl>  <dbl>  <dbl>
## 1 Calculated from study results, i.e., paired t-tests     301    301  -0.49
## 2 Calculated from study results, i.e., paired t-tests     301    301  -6.84
## 3 Calculated from study results, i.e., paired t-tests     301    301   7.11
##   sd_pool  d_post vd_post Wd_post      J  g_post vg_post Wg_post m_diff_t
##     <dbl>   <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>    <dbl>
## 1  0.6354 -0.7712 0.01428 0.01329 0.9975 -0.7693 0.01428 0.01329    -0.42
## 2 10.38   -0.6589 0.01401 0.01329 0.9975 -0.6573 0.01401 0.01329    -8.15
## 3 21.69    0.3278 0.01347 0.01329 0.9975  0.3270 0.01347 0.01329    12.5 
##   m_diff_c    d_DD   vd_DD   Wd_DD    g_DD   vg_DD   Wg_DD avg_cl_size
##      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>       <dbl>
## 1   -0.18  -0.3777 0.01434 0.01411 -0.3768 0.01434 0.01411           8
## 2   -2.96  -0.5000 0.01391 0.01350 -0.4987 0.01391 0.01350           8
## 3    3.230  0.4274 0.01398 0.01368  0.4264 0.01398 0.01368           8
##   avg_cl_type   icc icc_type n_covariates gamma_sqrt df_adj  omega gt_post
##   <chr>       <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>   <dbl>
## 1 Imputed       0.1 Imputed             1      0.974  287.4 0.9974 -0.7492
## 2 Imputed       0.1 Imputed             1      0.974  287.4 0.9974 -0.6401
## 3 Imputed       0.1 Imputed             1      0.974  287.4 0.9974  0.3185
##   vgt_post Wgt_post   gt_DD  vgt_DD  Wgt_DD   hg_DD   vhg_DD  h_DD df_DD
##      <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>    <dbl> <dbl> <dbl>
## 1  0.01816  0.01719 -0.3669 0.01847 0.01824 -0.1599 0.003480 287.4 287.4
## 2  0.01790  0.01719 -0.4857 0.01786 0.01745 -0.2160 0.003480 287.4 287.4
## 3  0.01736  0.01719  0.4152 0.01799 0.01769  0.1837 0.003480 287.4 287.4
##   n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop Wgt_DD_pop
##             <dbl> <chr>             <dbl>     <dbl>      <dbl>      <dbl>
## 1               1 eta               1.293   NA        NA         NA      
## 2               1 eta               1.293   -0.4209    0.01725    0.01696
## 3               1 eta               1.293   NA        NA         NA      
##   vary_id  
##   <chr>    
## 1 BSI (GSI)
## 2 BDI      
## 3 WHOQOL

Rosenblum et al. (2014) (Quality-checked)

Entering data from Table 3 (p. 85)

# Extracting data from table 3 p. 85 

Rosenblum2014 <- tibble(
  
  group = as.factor(rep(c(
    "Double Trouble in Recovery (DTR)",
    "waiting list control group"), 
    each = 1,5
  )),
  
  # sample = rep(c(
  #    "Total"
  #  "New York",
  #  "Michigan"
  #  ), each = 10,1),
  
  
  outcome = rep(c(
    "Days any alcohol past 30",
    "Days heavy alcohol past 30",
    "Days any drugs past 30",
    "Days any drugs or alcohol past 30",
    
    "Medication Adherence"
    
   # "RQOL (quality of life)c (total scale)" Only measured at follow-up 
   ), each = 2
 ),
 
 N = c(
   rep(c(91,70), each = 1,5) # Total 
   
   #rep(c(35, 32), each = 1,5), # New York
   
   #rep(c(56, 38), each = 1,5) # Michigan
   
 ),
 
 N_start = c(
   rep(c(113,90), each = 1,5) # Total 
   
  # rep(c(50, 40), each = 1,5), # New York
   
  # rep(c(50), each = 1,5) # Michigan
   
 ),
 
 m_pre = c(
   4.5, 6.5,
   2.6, 2.1,
   6.9, 7.7,
   9.3, 11.9,
   1.5, 1.5

   
 #  1.9, 3.9,
 #  0.3, 0.8,
 #  4.2, 7.1,
 #  4.7, 9.9,
 # 1.4, 1.4,

   
 #  6.1, 8.6, 
 # 4.0, 3.1,
 #  8.6, 8.2,
 # 12.2, 13.6,
 # 1.6, 1.6 

 ),
 
 sd_pre = c(
   8.2, 9.6,
   6.1, 5.1,
   9.3, 10.4,
   10.1, 11.2,
   0.5, 0.5

   
#   4.7, 7.2,
#   1.0, 1.8,
#   6.3, 10.2,
#   6.1, 10.8,
#   0.4, 0.4,
 
   
#   9.5, 11.0,
#   7.4, 6.5,
#   10.5, 10.6, 
#   11.0, 11.5,
#   0.5, 0.5
  
 ), 
 
 m_post = c(
   3.1, 6.1,
   1.1, 2.1, 
   5.5, 8.0,
   6.8, 11.3,
   1.4, 1.4
#   2.7, 2.0,
   
 #  1.6, 3.2,
#   0.3, 1.0, 
#   3.4, 8.0,
#   4.5, 10.2,
#   1.4, 1.3,
#   3.5, 3.2,
   
 #  4.0, 8.4,
#   1.5, 3.0, 
#   6.8, 8.0,
#   8.3, 12.2,
#   1.5, 1.6
#   2.1, 1.0
 ),

sd_post = c(
  6.4, 8.8,
  2.8, 6.0,
  9.2, 10.6,
  9.8, 11.1,
  0.4, 0.4
  # 1.8, 1.8,
  
#  4.4, 7.2,
#  1.3, 4.5,
 # 7.1, 10.2, 
#  7.8, 10.8, 
#  0.3, 0.3,
  #  1.5, 1.7,
  
#  7.3, 9.5, 
#  3.4, 7.0,
#  10.2, 11.1,
#  10.6, 11.4, 
#  0.5, 0.4
# 1.8, 1.3
),

#q = c(
#  rep(c(1), each = 1,10), # The baseline equivalent of the dependent variable was used as a covariate, p. 85
#  rep(c(2), each = 1, 4), # The baseline variable PTSD diagnosis was added as a covariate to these analyses because it was correlated with study condition and the #outcome variable, p. 85
#  rep(c(1), each  = 1,2),
#  rep(c(2), each = 1,2),
#  rep(c(1), each = 1,12)
#  )
); Rosenblum2014
## # A tibble: 10 × 8
##    group                            outcome                               N
##    <fct>                            <chr>                             <dbl>
##  1 Double Trouble in Recovery (DTR) Days any alcohol past 30             91
##  2 waiting list control group       Days any alcohol past 30             70
##  3 Double Trouble in Recovery (DTR) Days heavy alcohol past 30           91
##  4 waiting list control group       Days heavy alcohol past 30           70
##  5 Double Trouble in Recovery (DTR) Days any drugs past 30               91
##  6 waiting list control group       Days any drugs past 30               70
##  7 Double Trouble in Recovery (DTR) Days any drugs or alcohol past 30    91
##  8 waiting list control group       Days any drugs or alcohol past 30    70
##  9 Double Trouble in Recovery (DTR) Medication Adherence                 91
## 10 waiting list control group       Medication Adherence                 70
##    N_start m_pre sd_pre m_post sd_post
##      <dbl> <dbl>  <dbl>  <dbl>   <dbl>
##  1     113   4.5    8.2    3.1     6.4
##  2      90   6.5    9.6    6.1     8.8
##  3     113   2.6    6.1    1.1     2.8
##  4      90   2.1    5.1    2.1     6  
##  5     113   6.9    9.3    5.5     9.2
##  6      90   7.7   10.4    8      10.6
##  7     113   9.3   10.1    6.8     9.8
##  8      90  11.9   11.2   11.3    11.1
##  9     113   1.5    0.5    1.4     0.4
## 10      90   1.5    0.5    1.4     0.4
Rosenblum2014_wide <-
  Rosenblum2014 |> 
  mutate (group = case_match(
    group, "Double Trouble in Recovery (DTR)" ~ "t", "waiting list control group" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


Rosenblum2014_est <- 
  Rosenblum2014_wide |> 
  mutate(
    
  #  p_val = c(0.03, 0.11, 0.07, 0.02, 0.45, 
  #            0.84, 0.47, 0.07, 0.17, 0.73,
  #            0.01, 0.10, 0.31, 0.06, 0.15),
    

    analysis_plan = case_when(
      str_detect(outcome, "Days") ~ "Alcohol and drug abuse/misuse",
      str_detect(outcome, "Medication Adherence") ~ "Unused outcomes",
      .default = NA_character_
    ),
    
    study = "Rosenblum et al. 2014",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    
) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # all scores are reverted because lower scores is beneficial 
    m_post =  (m_post_t - m_post_c)*-1,
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # all scores are reverted because lower scores is beneficial 
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
       # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    vary_id = paste0(outcome)
  ) |> 
  ungroup(); Rosenblum2014_est
## # A tibble: 5 × 61
##   outcome                             N_t   N_c N_start_t N_start_c m_pre_t
##   <chr>                             <dbl> <dbl>     <dbl>     <dbl>   <dbl>
## 1 Days any alcohol past 30             91    70       113        90     4.5
## 2 Days heavy alcohol past 30           91    70       113        90     2.6
## 3 Days any drugs past 30               91    70       113        90     6.9
## 4 Days any drugs or alcohol past 30    91    70       113        90     9.3
## 5 Medication Adherence                 91    70       113        90     1.5
##   m_pre_c sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##     <dbl>    <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
## 1     6.5      8.2      9.6      3.1      6.1       6.4       8.8
## 2     2.1      6.1      5.1      1.1      2.1       2.8       6  
## 3     7.7      9.3     10.4      5.5      8         9.2      10.6
## 4    11.9     10.1     11.2      6.8     11.3       9.8      11.1
## 5     1.5      0.5      0.5      1.4      1.4       0.4       0.4
##   analysis_plan                 study                 effect_size
##   <chr>                         <chr>                 <chr>      
## 1 Alcohol and drug abuse/misuse Rosenblum et al. 2014 SMD        
## 2 Alcohol and drug abuse/misuse Rosenblum et al. 2014 SMD        
## 3 Alcohol and drug abuse/misuse Rosenblum et al. 2014 SMD        
## 4 Alcohol and drug abuse/misuse Rosenblum et al. 2014 SMD        
## 5 Unused outcomes               Rosenblum et al. 2014 SMD        
##   sd_used            main_es_method    ppcor ppcor_method N_total df_ind m_post
##   <chr>              <chr>             <dbl> <chr>          <dbl>  <dbl>  <dbl>
## 1 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          161    161    3  
## 2 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          161    161    1  
## 3 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          161    161    2.5
## 4 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          161    161    4.5
## 5 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          161    161    0  
##   sd_pool d_post vd_post Wd_post      J g_post vg_post Wg_post m_diff_t m_diff_c
##     <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1   7.536 0.3981 0.02577 0.02527 0.9953 0.3962 0.02576 0.02527   1.4      0.4000
## 2   4.479 0.2233 0.02543 0.02527 0.9953 0.2222 0.02543 0.02527   1.5      0     
## 3   9.832 0.2543 0.02548 0.02527 0.9953 0.2531 0.02547 0.02527   1.4     -0.3000
## 4  10.38  0.4334 0.02586 0.02527 0.9953 0.4313 0.02585 0.02527   2.5      0.6000
## 5   0.4   0      0.02527 0.02527 0.9953 0      0.02527 0.02527   0.1000   0.1000
##     d_DD   vd_DD   Wd_DD   g_DD   vg_DD   Wg_DD avg_cl_size avg_cl_type   icc
##    <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>       <dbl> <chr>       <dbl>
## 1 0.1327 0.02533 0.02527 0.1321 0.02533 0.02527           8 Imputed       0.1
## 2 0.3349 0.02562 0.02527 0.3333 0.02562 0.02527           8 Imputed       0.1
## 3 0.1729 0.02537 0.02527 0.1721 0.02537 0.02527           8 Imputed       0.1
## 4 0.1830 0.02538 0.02527 0.1821 0.02538 0.02527           8 Imputed       0.1
## 5 0      0.02527 0.02527 0      0.02527 0.02527           8 Imputed       0.1
##   icc_type n_covariates gamma_sqrt df_adj  omega gt_post vgt_post Wgt_post
##   <chr>           <dbl>      <dbl>  <dbl>  <dbl>   <dbl>    <dbl>    <dbl>
## 1 Imputed             1      0.976  152.4 0.9951  0.3866  0.03203  0.03154
## 2 Imputed             1      0.976  152.4 0.9951  0.2168  0.03170  0.03154
## 3 Imputed             1      0.976  152.4 0.9951  0.2469  0.03174  0.03154
## 4 Imputed             1      0.976  152.4 0.9951  0.4209  0.03212  0.03154
## 5 Imputed             1      0.976  152.4 0.9951  0       0.03154  0.03154
##    gt_DD  vgt_DD  Wgt_DD   hg_DD   vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD
##    <dbl>   <dbl>   <dbl>   <dbl>    <dbl> <dbl> <dbl>           <dbl> <chr>     
## 1 0.1289 0.03160 0.03154 0.05875 0.006560 152.4 152.4               1 eta       
## 2 0.3253 0.03189 0.03154 0.1481  0.006560 152.4 152.4               1 eta       
## 3 0.1679 0.03164 0.03154 0.07654 0.006560 152.4 152.4               1 eta       
## 4 0.1777 0.03165 0.03154 0.08099 0.006560 152.4 152.4               1 eta       
## 5 0      0.03154 0.03154 0       0.006560 152.4 152.4               1 eta       
##   adj_value_DD vary_id                          
##          <dbl> <chr>                            
## 1        1.248 Days any alcohol past 30         
## 2        1.248 Days heavy alcohol past 30       
## 3        1.248 Days any drugs past 30           
## 4        1.248 Days any drugs or alcohol past 30
## 5        1.248 Medication Adherence

Russinova et al. (2018) (Quality-checked)

Entering data from Table 4 (p. 203)

# Taken from table 
Russinova2018 <- tibble(
  group = rep(c("VEP", "Control group"),
              each = 1,10),
  
  
  outcome = rep(c("Empowerment scale", 
                  "ISMI Scale", 
                  "Work Hope Scale",
                  "Vocational identity", 
                  "Work motivation"), 
                each = 2, 2),

  timing = rep(c(
    #" 10 weeks post-baseline", 
                 "Post", 
                 "3m"), each = 10),
  
  
  N = c(
    # 10 weeks post-base
#    23, 25,
#    23, 25,
#    23, 25,
#    23, 24,
#    21, 23, 
  
  # Post intervention
    22, 26,
    22, 26,
    22, 26,
    22, 26,
    21, 24,
  
  # 3m followup
    22, 25,
    22, 25,
    22, 25,
    22, 25,
    21, 24),

  N_start = rep(c(
    24, 27,
    24, 26,
    24, 27,
    24, 27,
    21, 23
  ), each = 1,2),
  
  m_pre = rep(c(
    2.86, 2.84,
    2.11, 2.16, 
    4.38, 4.16,
    7.79, 7.04,
    3.09, 2.96),
    each = 1,2), 
  
  sd_pre = rep(c(
    .3, .21,
    .5, .43,
    1.13, .79,
    5.02, 5.31,
    .59, .47),
    each = 1,2),
  
  m_post = c(
    # 10 weeks post-base
  #  3.02, 2.85,
  #  1.93, 2.17, 
  #  4.63, 4.26,
  #  8.61, 6.88,
  #  3.11, 2.97,
  
    # Post intervention
    2.98, 2.92,
    1.86, 2.23,
    4.64, 4.29,
    9.09, 6.77,
    3.15, 3.09,
    
    # 3m followup
    2.96, 2.88,
    1.92, 2.08,
    4.67, 4.58,
    9.23, 8.32,
    3.17, 3.04
    ),
  
  sd_post = c(
    # 10 weeks post-base
  #  .34, .21,
  #  .43, .44,
  #  1.21, .96,
  #  4.49, 4.48,
  #  .67, .49,
    
    # Post intervention
    .34, .24,
    .44, .41,
    1.29, 1.15,
    5.08, 4.99,
    .58, .55,
    
    # 3m followup
    .34, .29,
    .5, .43,
    1.24, .94,
    5.05, 5.17,
    .6, .5) 
  
  ); Russinova2018
## # A tibble: 20 × 9
##    group         outcome             timing     N N_start m_pre sd_pre m_post
##    <chr>         <chr>               <chr>  <dbl>   <dbl> <dbl>  <dbl>  <dbl>
##  1 VEP           Empowerment scale   Post      22      24  2.86   0.3    2.98
##  2 Control group Empowerment scale   Post      26      27  2.84   0.21   2.92
##  3 VEP           ISMI Scale          Post      22      24  2.11   0.5    1.86
##  4 Control group ISMI Scale          Post      26      26  2.16   0.43   2.23
##  5 VEP           Work Hope Scale     Post      22      24  4.38   1.13   4.64
##  6 Control group Work Hope Scale     Post      26      27  4.16   0.79   4.29
##  7 VEP           Vocational identity Post      22      24  7.79   5.02   9.09
##  8 Control group Vocational identity Post      26      27  7.04   5.31   6.77
##  9 VEP           Work motivation     Post      21      21  3.09   0.59   3.15
## 10 Control group Work motivation     Post      24      23  2.96   0.47   3.09
## 11 VEP           Empowerment scale   3m        22      24  2.86   0.3    2.96
## 12 Control group Empowerment scale   3m        25      27  2.84   0.21   2.88
## 13 VEP           ISMI Scale          3m        22      24  2.11   0.5    1.92
## 14 Control group ISMI Scale          3m        25      26  2.16   0.43   2.08
## 15 VEP           Work Hope Scale     3m        22      24  4.38   1.13   4.67
## 16 Control group Work Hope Scale     3m        25      27  4.16   0.79   4.58
## 17 VEP           Vocational identity 3m        22      24  7.79   5.02   9.23
## 18 Control group Vocational identity 3m        25      27  7.04   5.31   8.32
## 19 VEP           Work motivation     3m        21      21  3.09   0.59   3.17
## 20 Control group Work motivation     3m        24      23  2.96   0.47   3.04
##    sd_post
##      <dbl>
##  1    0.34
##  2    0.24
##  3    0.44
##  4    0.41
##  5    1.29
##  6    1.15
##  7    5.08
##  8    4.99
##  9    0.58
## 10    0.55
## 11    0.34
## 12    0.29
## 13    0.5 
## 14    0.43
## 15    1.24
## 16    0.94
## 17    5.05
## 18    5.17
## 19    0.6 
## 20    0.5
# Turning to wideformat
Russinova2018_wide <- Russinova2018 |> 
      mutate(group = case_match(group, "VEP" ~ "t", "Control group" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
      )

Russinova2018_est <- Russinova2018_wide |> 
  mutate(
    # Group effect is estimated across time scores.
    # df1 = c(
    #  rep(c(NA_real_), each = 5,1)
    #,
    #  rep(c(1), each = 5,1)),
    
    # df2 = c(
    #  rep(c(NA_real_), each = 5,1)
    # ,
    #  c(91,
    #    90,
    #    92,
    #    91,
    #    75)),
    
  #  F_val = c(
  #    rep(c(NA_real_), each = 5,1),
      
  #    6.65,
  #    5.84,
  #    1.39,
  #    1.98,
  #    0.08    
  #  ),
  #  p_val_F = c(
      
  #     rep(c(NA_real_), each = 5,1),
      
  #    0.01,
  #    0.02,
  #    0.24,
  #    0.16,
  #    0.78
  #  ),
    
  #  d = c(
  #     rep(c(NA_real_), each = 5,1),
       
  #     0.39,
  #     -0.65,
  #     0.32,
  #     0.41,
  #     0.30
  #  ),
    study = "Russinova et al. 2018",
    
    analysis_plan = case_when(
      str_detect(outcome, "Empowerment|Work Hope|Work motivation") ~ "Hope, Empowerment & Self-efficacy",
      str_detect(outcome, "ISMI Scale") ~ "Self-esteem",
      str_detect(outcome, "Vocational identity") ~ "Employment",
      .default = NA_character_
    ),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    
  ) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # 
    m_post = if_else(!outcome %in% c("ISMI Scale"), m_post_t - m_post_c,
                     (m_post_t - m_post_c)*-1),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # The outcome BSI and BDI are reverted because lower score is beneficial  
    m_diff_t = if_else(!outcome %in% c("ISMI Scale"),
                        m_post_t - m_pre_t,(m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(!outcome %in% c("ISMI Scale"),
                       m_post_c - m_pre_c, (m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    ) |> 
  
  rowwise() |> 
  
  mutate(
      # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ), 
    
    vary_id = paste0(outcome, "/", timing)
  ) |> 
    ungroup(); Russinova2018_est
## # A tibble: 10 × 62
##    outcome             timing   N_t   N_c N_start_t N_start_c m_pre_t m_pre_c
##    <chr>               <chr>  <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>
##  1 Empowerment scale   Post      22    26        24        27    2.86    2.84
##  2 ISMI Scale          Post      22    26        24        26    2.11    2.16
##  3 Work Hope Scale     Post      22    26        24        27    4.38    4.16
##  4 Vocational identity Post      22    26        24        27    7.79    7.04
##  5 Work motivation     Post      21    24        21        23    3.09    2.96
##  6 Empowerment scale   3m        22    25        24        27    2.86    2.84
##  7 ISMI Scale          3m        22    25        24        26    2.11    2.16
##  8 Work Hope Scale     3m        22    25        24        27    4.38    4.16
##  9 Vocational identity 3m        22    25        24        27    7.79    7.04
## 10 Work motivation     3m        21    24        21        23    3.09    2.96
##    sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c study                
##       <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl> <chr>                
##  1     0.3      0.21     2.98     2.92      0.34      0.24 Russinova et al. 2018
##  2     0.5      0.43     1.86     2.23      0.44      0.41 Russinova et al. 2018
##  3     1.13     0.79     4.64     4.29      1.29      1.15 Russinova et al. 2018
##  4     5.02     5.31     9.09     6.77      5.08      4.99 Russinova et al. 2018
##  5     0.59     0.47     3.15     3.09      0.58      0.55 Russinova et al. 2018
##  6     0.3      0.21     2.96     2.88      0.34      0.29 Russinova et al. 2018
##  7     0.5      0.43     1.92     2.08      0.5       0.43 Russinova et al. 2018
##  8     1.13     0.79     4.67     4.58      1.24      0.94 Russinova et al. 2018
##  9     5.02     5.31     9.23     8.32      5.05      5.17 Russinova et al. 2018
## 10     0.59     0.47     3.17     3.04      0.6       0.5  Russinova et al. 2018
##    analysis_plan                     effect_size sd_used           
##    <chr>                             <chr>       <chr>             
##  1 Hope, Empowerment & Self-efficacy SMD         Pooled posttest SD
##  2 Self-esteem                       SMD         Pooled posttest SD
##  3 Hope, Empowerment & Self-efficacy SMD         Pooled posttest SD
##  4 Employment                        SMD         Pooled posttest SD
##  5 Hope, Empowerment & Self-efficacy SMD         Pooled posttest SD
##  6 Hope, Empowerment & Self-efficacy SMD         Pooled posttest SD
##  7 Self-esteem                       SMD         Pooled posttest SD
##  8 Hope, Empowerment & Self-efficacy SMD         Pooled posttest SD
##  9 Employment                        SMD         Pooled posttest SD
## 10 Hope, Empowerment & Self-efficacy SMD         Pooled posttest SD
##    main_es_method    ppcor ppcor_method N_total df_ind  m_post sd_pool  d_post
##    <chr>             <dbl> <chr>          <dbl>  <dbl>   <dbl>   <dbl>   <dbl>
##  1 Raw diff-in-diffs   0.5 Imputed           48     48 0.06000  0.2900 0.2069 
##  2 Raw diff-in-diffs   0.5 Imputed           48     48 0.37     0.4240 0.8727 
##  3 Raw diff-in-diffs   0.5 Imputed           48     48 0.3500   1.216  0.2878 
##  4 Raw diff-in-diffs   0.5 Imputed           48     48 2.32     5.031  0.4611 
##  5 Raw diff-in-diffs   0.5 Imputed           45     45 0.06000  0.5642 0.1064 
##  6 Raw diff-in-diffs   0.5 Imputed           47     47 0.08000  0.3143 0.2545 
##  7 Raw diff-in-diffs   0.5 Imputed           47     47 0.1600   0.4640 0.3448 
##  8 Raw diff-in-diffs   0.5 Imputed           47     47 0.09000  1.090  0.08254
##  9 Raw diff-in-diffs   0.5 Imputed           47     47 0.91     5.114  0.1779 
## 10 Raw diff-in-diffs   0.5 Imputed           45     45 0.1300   0.5488 0.2369 
##    vd_post Wd_post      J  g_post vg_post Wg_post m_diff_t m_diff_c     d_DD
##      <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>
##  1 0.08436 0.08392 0.9843 0.2037  0.08435 0.08392  0.1200   0.08000  0.1379 
##  2 0.09185 0.08392 0.9843 0.8590  0.09160 0.08392  0.2500  -0.07000  0.7548 
##  3 0.08478 0.08392 0.9843 0.2833  0.08475 0.08392  0.2600   0.1300   0.1069 
##  4 0.08613 0.08392 0.9843 0.4539  0.08606 0.08392  1.3     -0.2700   0.3120 
##  5 0.08941 0.08929 0.9832 0.1046  0.08941 0.08929  0.06000  0.1300  -0.1241 
##  6 0.08614 0.08545 0.9840 0.2504  0.08612 0.08545  0.1000   0.04000  0.1909 
##  7 0.08672 0.08545 0.9840 0.3393  0.08668 0.08545  0.19     0.08000  0.2371 
##  8 0.08553 0.08545 0.9840 0.08122 0.08552 0.08545  0.29     0.42    -0.1192 
##  9 0.08579 0.08545 0.9840 0.1751  0.08578 0.08545  1.44     1.28     0.03128
## 10 0.08991 0.08929 0.9832 0.2329  0.08989 0.08929  0.08000  0.08000  0      
##      vd_DD   Wd_DD     g_DD   vg_DD   Wg_DD avg_cl_size avg_cl_type   icc
##      <dbl>   <dbl>    <dbl>   <dbl>   <dbl>       <dbl> <chr>       <dbl>
##  1 0.08411 0.08392  0.1358  0.08411 0.08392           8 Imputed       0.1
##  2 0.08985 0.08392  0.7429  0.08967 0.08392           8 Imputed       0.1
##  3 0.08404 0.08392  0.1052  0.08403 0.08392           8 Imputed       0.1
##  4 0.08493 0.08392  0.3071  0.08490 0.08392           8 Imputed       0.1
##  5 0.08946 0.08929 -0.1220  0.08945 0.08929           8 Imputed       0.1
##  6 0.08584 0.08545  0.1878  0.08583 0.08545           8 Imputed       0.1
##  7 0.08605 0.08545  0.2333  0.08603 0.08545           8 Imputed       0.1
##  8 0.08561 0.08545 -0.1173  0.08560 0.08545           8 Imputed       0.1
##  9 0.08546 0.08545  0.03078 0.08546 0.08545           8 Imputed       0.1
## 10 0.08929 0.08929  0       0.08929 0.08929           8 Imputed       0.1
##    icc_type n_covariates gamma_sqrt df_adj  omega gt_post vgt_post Wgt_post
##    <chr>           <dbl>      <dbl>  <dbl>  <dbl>   <dbl>    <dbl>    <dbl>
##  1 Imputed             1      0.965  44.79 0.9832 0.1963    0.1123   0.1119
##  2 Imputed             1      0.965  44.79 0.9832 0.8280    0.1195   0.1119
##  3 Imputed             1      0.965  44.79 0.9832 0.2731    0.1127   0.1119
##  4 Imputed             1      0.965  44.79 0.9832 0.4375    0.1140   0.1119
##  5 Imputed             1      0.964  41.87 0.9820 0.1007    0.1186   0.1185
##  6 Imputed             1      0.965  43.79 0.9828 0.2414    0.1140   0.1133
##  7 Imputed             1      0.965  43.79 0.9828 0.3270    0.1145   0.1133
##  8 Imputed             1      0.965  43.79 0.9828 0.07828   0.1134   0.1133
##  9 Imputed             1      0.965  43.79 0.9828 0.1687    0.1136   0.1133
## 10 Imputed             1      0.964  41.87 0.9820 0.2242    0.1191   0.1185
##       gt_DD vgt_DD Wgt_DD    hg_DD  vhg_DD  h_DD df_DD n_covariates_DD
##       <dbl>  <dbl>  <dbl>    <dbl>   <dbl> <dbl> <dbl>           <dbl>
##  1  0.1309  0.1121 0.1119  0.05845 0.02233 44.79 44.79               1
##  2  0.7161  0.1176 0.1119  0.3173  0.02233 44.79 44.79               1
##  3  0.1014  0.1120 0.1119  0.04531 0.02233 44.79 44.79               1
##  4  0.2961  0.1128 0.1119  0.1321  0.02233 44.79 44.79               1
##  5 -0.1175  0.1186 0.1185 -0.05272 0.02388 41.87 41.87               1
##  6  0.1810  0.1137 0.1133  0.08122 0.02284 43.79 43.79               1
##  7  0.2248  0.1139 0.1133  0.1009  0.02284 43.79 43.79               1
##  8 -0.1131  0.1135 0.1133 -0.05075 0.02284 43.79 43.79               1
##  9  0.02967 0.1133 0.1133  0.01332 0.02284 43.79 43.79               1
## 10  0       0.1185 0.1185  0       0.02388 41.87 41.87               1
##    adj_fct_DD adj_value_DD vary_id                 
##    <chr>             <dbl> <chr>                   
##  1 eta               1.333 Empowerment scale/Post  
##  2 eta               1.333 ISMI Scale/Post         
##  3 eta               1.333 Work Hope Scale/Post    
##  4 eta               1.333 Vocational identity/Post
##  5 eta               1.327 Work motivation/Post    
##  6 eta               1.326 Empowerment scale/3m    
##  7 eta               1.326 ISMI Scale/3m           
##  8 eta               1.326 Work Hope Scale/3m      
##  9 eta               1.326 Vocational identity/3m  
## 10 eta               1.327 Work motivation/3m

Rüsch et al. (2019) (Quality-checked)

Data was extracted from Table 1 (p. 35). NOTE: We suspect that the reported F values from ANCOVA for Job-search and Help-seeking at Time-3 (T3) are prone to error. Since they seem to be too small compared with the reported change score differences. Thus, we did not use these values for sampling variance estimation of the corresponding effect sizes.

# Data from Table 1 containing means and standard deviation on page 335
# The table also contains partial η2 values, p-values and F-test

Rüsch2019 <- 
  tibble(
    group = rep(c("Intervention", 
                  "Control"), 
                each = 1,12),
    
    timing = rep(c("6w", 
                   "6 months"), 
                 each = 12,1),
    
    outcome = rep(c("Job_search", 
                    "Help_seeking", 
                    "SISR",
                    "SSMIS_SF",
                    "CES-D", 
                    "BHS"  
                    #  "Secrecy"
    ), 
    each = 2,2 ),
    
    N = c(rep(c(
      18, 17), # 6 weeks 
      times = 6), 
      rep(c(
        
        20, 13), # 12 months
        times = 6)),
    
    N_start = rep(c(
      23, 19
    ), each = 1,12),
    
    m_pre = rep(c(
      3.1, 3.3, # Job-search self-efficacy (1–5)
      3.4, 3.0,  # Help-seeking intentions (1–7)
      15.3, 16.3, # Recovery (SISR: 4–24)
      17.3, 17.3,  # Self-stigma (SSMIS-SF: 5–45)
      39.2, 41.4, # Depressive symptoms (CES-D: 15–60)
      14.9, 14.1 # Hopelessness (BHS: 4–24)
      #    4.0, 3.7   # Link’s 5-item Secrecy Scale
    ), each = 1,2),
    
    sd_pre = rep(c(
      0.8, 0.8,
      1, 1,
      4.1, 4.2,
      4.3, 8.1,
      7.4, 7.2,
      3.6, 4.5 
      #    1.2, 1.6
    ), each = 1,2),
    
    m_post = c(
      # 3 Weeks
      #  3.2, 3.2, 
      #  3.6, 3,
      #  16.1, 15.9, 
      #  15.3, 16.9, 
      #  35.2, 39.1,
      #  13.8, 13.7,
      #  3.8, 3.7
      
      # 6 weeks
      3.4, 3.3, 
      3.5, 3.2, 
      17.8, 16.1, 
      14.6, 19.1, 
      31.5, 40.1, 
      12.6, 14.2, 
      #    3.5, 3.9,
      
      # 6 months 
      3.2, 3.2,
      3.3, 3.1,
      17.3, 15.7,
      15.4, 18.3,
      32, 37.5,
      12.3, 14.4
      #    3.7, 4.4
      
    ),
    
    
    sd_post = c(
      # 3 weeks
      # 0.7, 0.9, 
      # 1, 0.9, 
      # 4.2, 3.6, 
      # 6.2, 8, 
      # 8.8, 10.2, 
      # 3.9, 4.6, 
      # 1, 1.7
      
      # 6 weeks
      0.8, 0.9,
      1, 1, 
      3.4, 4.4,
      7.9, 8.7,
      8.5, 10.1,
      3.6, 5, 
      #  1.3, 1.7,
      
      # 6 months
      1, 0.9, 
      0.6, 0.9,
      3.5, 2.6,
      7, 9.6,
      7.6, 9.8,
      4.2, 3
      #  0.9, 1.4
    )
  ); Rüsch2019
## # A tibble: 24 × 9
##    group        timing   outcome          N N_start m_pre sd_pre m_post sd_post
##    <chr>        <chr>    <chr>        <dbl>   <dbl> <dbl>  <dbl>  <dbl>   <dbl>
##  1 Intervention 6w       Job_search      18      23   3.1    0.8    3.4     0.8
##  2 Control      6w       Job_search      17      19   3.3    0.8    3.3     0.9
##  3 Intervention 6w       Help_seeking    18      23   3.4    1      3.5     1  
##  4 Control      6w       Help_seeking    17      19   3      1      3.2     1  
##  5 Intervention 6w       SISR            18      23  15.3    4.1   17.8     3.4
##  6 Control      6w       SISR            17      19  16.3    4.2   16.1     4.4
##  7 Intervention 6w       SSMIS_SF        18      23  17.3    4.3   14.6     7.9
##  8 Control      6w       SSMIS_SF        17      19  17.3    8.1   19.1     8.7
##  9 Intervention 6w       CES-D           18      23  39.2    7.4   31.5     8.5
## 10 Control      6w       CES-D           17      19  41.4    7.2   40.1    10.1
## 11 Intervention 6w       BHS             18      23  14.9    3.6   12.6     3.6
## 12 Control      6w       BHS             17      19  14.1    4.5   14.2     5  
## 13 Intervention 6 months Job_search      20      23   3.1    0.8    3.2     1  
## 14 Control      6 months Job_search      13      19   3.3    0.8    3.2     0.9
## 15 Intervention 6 months Help_seeking    20      23   3.4    1      3.3     0.6
## 16 Control      6 months Help_seeking    13      19   3      1      3.1     0.9
## 17 Intervention 6 months SISR            20      23  15.3    4.1   17.3     3.5
## 18 Control      6 months SISR            13      19  16.3    4.2   15.7     2.6
## 19 Intervention 6 months SSMIS_SF        20      23  17.3    4.3   15.4     7  
## 20 Control      6 months SSMIS_SF        13      19  17.3    8.1   18.3     9.6
## 21 Intervention 6 months CES-D           20      23  39.2    7.4   32       7.6
## 22 Control      6 months CES-D           13      19  41.4    7.2   37.5     9.8
## 23 Intervention 6 months BHS             20      23  14.9    3.6   12.3     4.2
## 24 Control      6 months BHS             13      19  14.1    4.5   14.4     3
# Turning data into wide format

Rüsch2019_wide <- Rüsch2019 |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


Rüsch2019_est <- 
  Rüsch2019_wide |> 
  mutate(
    
    analysis_plan = case_when(
      str_detect(outcome, "CES-D") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "Job_search|Help_seeking|BHS|SISR") ~ "Hope, Empowerment & Self-efficacy",
      str_detect(outcome, "SSMIS_SF") ~ "Self-esteem",
      .default = NA_character_
    ),
    
    # ANCOVA estimates taken from table 1, p. 335
    F_val = c(
      # 12 weeks
      1.4, 0.2, 4.6, 10.8, 6.3, 3.4,
      # 6 months
      0.01, 0.04, 1.9, 1.5, 0.9, 2.4
    ),
    
    #eta_sqrt = c(
    # 12 weeks
    # 0.04, 0.007, 0.13, 0.25, 0.17, 0.10,
    
    # 6 months
    #  0.01, 0.01, 0.06, 0.05, 0.03, 0.07
    #),
    
    #p_val_f = c(
    # 12 weeks
    # 0.24, 0.64, 0.039, 0.08, 0.17, 0.08,
    
    # 6 months
    #  0.96, 0.85, 0.17, 0.23, 0.35, 0.14
    
    
    
    study = "Rüsch et al. 2019"
  ) |> 
 # arrange(outcome, desc(timing)) |> 
  rowwise() |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    ppcor = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      ppcor_imp,
      NA_real_
    ),
    ppcor_method = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      "Imputed - We did not trust the reported F values",
      "F values from ANCOVA was used for variance estimation"
    ),
    
    m_post = case_when(
      outcome %in% c("Job_search", "Help_seeking", "SISR") ~ (m_post_t - m_post_c), # Higher is beneficial
      outcome %in% c("SSMIS_SF", "CES-D", "BHS") ~ (m_post_t - m_post_c) * -1), # Lower is beneficial
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # The outcome BSI and BDI are reverted because lower score is beneficial  
    m_diff_t = case_when(
      outcome %in% c("Job_search", "Help_seeking", "SISR") ~ (m_post_t - m_pre_t), # Higher is beneficial
      outcome %in% c("SSMIS_SF", "CES-D", "BHS") ~ (m_post_t - m_pre_t)*-1 # Lower is beneficial
    ), 
    
    m_diff_c = case_when(
      outcome %in% c("Job_search", "Help_seeking", "SISR") ~ (m_post_c - m_pre_c), # Higher is beneficial
      outcome %in% c("SSMIS_SF", "CES-D", "BHS") ~ (m_post_c - m_pre_c)*-1 # Lower is beneficial
    ), 
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
      d_DD^2/F_val + d_DD^2/(2*df_ind)
    ),
    Wd_DD = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      2*(1-ppcor) * (1/N_t + 1/N_c),
      d_DD^2/F_val
    ),
    
    g_DD = J * d_DD, 
    vg_DD = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
      g_DD^2/F_val + g_DD^2/(2*df_ind)
    ),
    Wg_DD = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      2*(1-ppcor) * (1/N_t + 1/N_c),
      g_DD^2/F_val
    )
  ) |> 
  rowwise() |> 
  mutate(
    # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    
    
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    
    if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      VIVECampbell::vgt_smd_1armcluster(
        N_cl_grp = N_t, 
        N_ind_grp = N_c, 
        avg_grp_size = avg_cl_size, 
        ICC = icc, 
        g = gt_DD, 
        model = "DiD",
        cluster_adj = FALSE,
        prepost_cor = ppcor,
        q = n_covariates,
        add_name_to_vars = "_DD",
        vars = -var_term1_DD
      ),
      VIVECampbell::vgt_smd_1armcluster(
        N_cl_grp = N_t, 
        N_ind_grp = N_c, 
        avg_grp_size = avg_cl_size, 
        ICC = icc, 
        g = gt_DD, 
        model = "DiD",
        cluster_adj = FALSE,
        F_val = F_val,
        q = n_covariates,
        add_name_to_vars = "_DD",
        vars = -var_term1_DD
      )
    ),
    vary_id = paste0(outcome, "/", timing)
  ) |> 
  ungroup(); Rüsch2019_est
## # A tibble: 12 × 63
##    timing   outcome        N_t   N_c N_start_t N_start_c m_pre_t m_pre_c
##    <chr>    <chr>        <dbl> <dbl>     <dbl>     <dbl>   <dbl>   <dbl>
##  1 6w       Job_search      18    17        23        19     3.1     3.3
##  2 6w       Help_seeking    18    17        23        19     3.4     3  
##  3 6w       SISR            18    17        23        19    15.3    16.3
##  4 6w       SSMIS_SF        18    17        23        19    17.3    17.3
##  5 6w       CES-D           18    17        23        19    39.2    41.4
##  6 6w       BHS             18    17        23        19    14.9    14.1
##  7 6 months Job_search      20    13        23        19     3.1     3.3
##  8 6 months Help_seeking    20    13        23        19     3.4     3  
##  9 6 months SISR            20    13        23        19    15.3    16.3
## 10 6 months SSMIS_SF        20    13        23        19    17.3    17.3
## 11 6 months CES-D           20    13        23        19    39.2    41.4
## 12 6 months BHS             20    13        23        19    14.9    14.1
##    sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##       <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
##  1      0.8      0.8      3.4      3.3       0.8       0.9
##  2      1        1        3.5      3.2       1         1  
##  3      4.1      4.2     17.8     16.1       3.4       4.4
##  4      4.3      8.1     14.6     19.1       7.9       8.7
##  5      7.4      7.2     31.5     40.1       8.5      10.1
##  6      3.6      4.5     12.6     14.2       3.6       5  
##  7      0.8      0.8      3.2      3.2       1         0.9
##  8      1        1        3.3      3.1       0.6       0.9
##  9      4.1      4.2     17.3     15.7       3.5       2.6
## 10      4.3      8.1     15.4     18.3       7         9.6
## 11      7.4      7.2     32       37.5       7.6       9.8
## 12      3.6      4.5     12.3     14.4       4.2       3  
##    analysis_plan                         F_val study             N_total df_ind
##    <chr>                                 <dbl> <chr>               <dbl>  <dbl>
##  1 Hope, Empowerment & Self-efficacy      1.4  Rüsch et al. 2019      35     35
##  2 Hope, Empowerment & Self-efficacy      0.2  Rüsch et al. 2019      35     35
##  3 Hope, Empowerment & Self-efficacy      4.6  Rüsch et al. 2019      35     35
##  4 Self-esteem                           10.8  Rüsch et al. 2019      35     35
##  5 All mental health outcomes/Depression  6.3  Rüsch et al. 2019      35     35
##  6 Hope, Empowerment & Self-efficacy      3.4  Rüsch et al. 2019      35     35
##  7 Hope, Empowerment & Self-efficacy      0.01 Rüsch et al. 2019      33     33
##  8 Hope, Empowerment & Self-efficacy      0.04 Rüsch et al. 2019      33     33
##  9 Hope, Empowerment & Self-efficacy      1.9  Rüsch et al. 2019      33     33
## 10 Self-esteem                            1.5  Rüsch et al. 2019      33     33
## 11 All mental health outcomes/Depression  0.9  Rüsch et al. 2019      33     33
## 12 Hope, Empowerment & Self-efficacy      2.4  Rüsch et al. 2019      33     33
##    effect_size sd_used            main_es_method    ppcor
##    <chr>       <chr>              <chr>             <dbl>
##  1 SMD         Pooled posttest SD Raw diff-in-diffs  NA  
##  2 SMD         Pooled posttest SD Raw diff-in-diffs  NA  
##  3 SMD         Pooled posttest SD Raw diff-in-diffs  NA  
##  4 SMD         Pooled posttest SD Raw diff-in-diffs  NA  
##  5 SMD         Pooled posttest SD Raw diff-in-diffs  NA  
##  6 SMD         Pooled posttest SD Raw diff-in-diffs  NA  
##  7 SMD         Pooled posttest SD Raw diff-in-diffs   0.5
##  8 SMD         Pooled posttest SD Raw diff-in-diffs   0.5
##  9 SMD         Pooled posttest SD Raw diff-in-diffs  NA  
## 10 SMD         Pooled posttest SD Raw diff-in-diffs  NA  
## 11 SMD         Pooled posttest SD Raw diff-in-diffs  NA  
## 12 SMD         Pooled posttest SD Raw diff-in-diffs  NA  
##    ppcor_method                                          m_post sd_pool d_post
##    <chr>                                                  <dbl>   <dbl>  <dbl>
##  1 F values from ANCOVA was used for variance estimation 0.1000  0.8500 0.1177
##  2 F values from ANCOVA was used for variance estimation 0.3000  1      0.3000
##  3 F values from ANCOVA was used for variance estimation 1.7     3.917  0.4340
##  4 F values from ANCOVA was used for variance estimation 4.5     8.298  0.5423
##  5 F values from ANCOVA was used for variance estimation 8.6     9.310  0.9237
##  6 F values from ANCOVA was used for variance estimation 1.6     4.336  0.3690
##  7 Imputed - We did not trust the reported F values      0       0.9625 0     
##  8 Imputed - We did not trust the reported F values      0.2000  0.7309 0.2736
##  9 F values from ANCOVA was used for variance estimation 1.600   3.182  0.5028
## 10 F values from ANCOVA was used for variance estimation 2.9     8.106  0.3578
## 11 F values from ANCOVA was used for variance estimation 5.5     8.519  0.6456
## 12 F values from ANCOVA was used for variance estimation 2.1     3.781  0.5554
##    vd_post Wd_post      J g_post vg_post Wg_post m_diff_t m_diff_c    d_DD
##      <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>   <dbl>
##  1  0.1146  0.1144 0.9784 0.1151  0.1146  0.1144   0.3000   0       0.3530
##  2  0.1157  0.1144 0.9784 0.2935  0.1156  0.1144   0.1000   0.2000 -0.1000
##  3  0.1171  0.1144 0.9784 0.4247  0.1170  0.1144   2.5     -0.2000  0.6893
##  4  0.1186  0.1144 0.9784 0.5306  0.1184  0.1144   2.7     -1.8     0.5423
##  5  0.1266  0.1144 0.9784 0.9038  0.1260  0.1144   7.7      1.300   0.6874
##  6  0.1163  0.1144 0.9784 0.3611  0.1162  0.1144   2.3     -0.1000  0.5536
##  7  0.1269  0.1269 0.9771 0       0.1269  0.1269   0.1000  -0.1000  0.2078
##  8  0.1281  0.1269 0.9771 0.2674  0.1280  0.1269  -0.1000   0.1000 -0.2736
##  9  0.1308  0.1269 0.9771 0.4913  0.1306  0.1269   2       -0.6000  0.8171
## 10  0.1289  0.1269 0.9771 0.3496  0.1288  0.1269   1.9     -1       0.3578
## 11  0.1332  0.1269 0.9771 0.6308  0.1330  0.1269   7.2      3.9     0.3874
## 12  0.1316  0.1269 0.9771 0.5427  0.1314  0.1269   2.6     -0.3000  0.7670
##      vd_DD   Wd_DD     g_DD   vg_DD   Wg_DD avg_cl_size avg_cl_type   icc
##      <dbl>   <dbl>    <dbl>   <dbl>   <dbl>       <dbl> <chr>       <dbl>
##  1 0.09077 0.08899  0.3453  0.08689 0.08519           8 Imputed       0.1
##  2 0.05014 0.05000 -0.09784 0.04800 0.04787           8 Imputed       0.1
##  3 0.1101  0.1033   0.6744  0.1054  0.09889           8 Imputed       0.1
##  4 0.03144 0.02723  0.5306  0.03009 0.02607           8 Imputed       0.1
##  5 0.08176 0.07501  0.6726  0.07827 0.07180           8 Imputed       0.1
##  6 0.09450 0.09012  0.5416  0.09047 0.08628           8 Imputed       0.1
##  7 0.1276  0.1269   0.2030  0.1275  0.1269            8 Imputed       0.1
##  8 0.1281  0.1269  -0.2674  0.1280  0.1269            8 Imputed       0.1
##  9 0.3615  0.3514   0.7984  0.3452  0.3355            8 Imputed       0.1
## 10 0.08727 0.08533  0.3496  0.08332 0.08146           8 Imputed       0.1
## 11 0.1690  0.1667   0.3785  0.1613  0.1592            8 Imputed       0.1
## 12 0.2540  0.2451   0.7494  0.2425  0.2340            8 Imputed       0.1
##    icc_type n_covariates gamma_sqrt df_adj  omega gt_post vgt_post Wgt_post
##    <chr>           <dbl>      <dbl>  <dbl>  <dbl>   <dbl>    <dbl>    <dbl>
##  1 Imputed             1      0.965  32.13 0.9765  0.1109   0.1476   0.1474
##  2 Imputed             1      0.965  32.13 0.9765  0.2827   0.1487   0.1474
##  3 Imputed             1      0.965  32.13 0.9765  0.4090   0.1500   0.1474
##  4 Imputed             1      0.965  32.13 0.9765  0.5110   0.1515   0.1474
##  5 Imputed             1      0.965  32.13 0.9765  0.8704   0.1592   0.1474
##  6 Imputed             1      0.965  32.13 0.9765  0.3477   0.1493   0.1474
##  7 Imputed             1      0.969  30    0.9748  0        0.1542   0.1542
##  8 Imputed             1      0.969  30    0.9748  0.2585   0.1553   0.1542
##  9 Imputed             1      0.969  30    0.9748  0.4750   0.1580   0.1542
## 10 Imputed             1      0.969  30    0.9748  0.3379   0.1561   0.1542
## 11 Imputed             1      0.969  30    0.9748  0.6098   0.1604   0.1542
## 12 Imputed             1      0.969  30    0.9748  0.5246   0.1588   0.1542
##       gt_DD  vgt_DD  Wgt_DD    hg_DD  vhg_DD  h_DD df_DD n_covariates_DD
##       <dbl>   <dbl>   <dbl>    <dbl>   <dbl> <dbl> <dbl>           <dbl>
##  1  0.3326  0.1036  0.1018   0.1833  0.03112 32.13 32.13               1
##  2 -0.09423 0.05736 0.05723 -0.06946 0.03112 32.13 32.13               1
##  3  0.6496  0.1248  0.1182   0.3303  0.03112 32.13 32.13               1
##  4  0.5110  0.03523 0.03117  0.5002  0.03112 32.13 32.13               1
##  5  0.6478  0.09238 0.08585  0.3852  0.03112 32.13 32.13               1
##  6  0.5216  0.1074  0.1032   0.2846  0.03112 32.13 32.13               1
##  7  0.1963  0.1549  0.1542   0.09119 0.03333 30    30                  1
##  8 -0.2585  0.1553  0.1542  -0.1200  0.03333 30    30                  1
##  9  0.7718  0.3909  0.3809   0.2273  0.03333 30    30                  1
## 10  0.3379  0.09440 0.09250  0.2022  0.03333 30    30                  1
## 11  0.3659  0.1830  0.1807   0.1568  0.03333 30    30                  1
## 12  0.7245  0.2745  0.2657   0.2552  0.03333 30    30                  1
##    adj_fct_DD adj_value_DD vary_id              
##    <chr>             <dbl> <chr>                
##  1 eta               1.289 Job_search/6w        
##  2 eta               1.289 Help_seeking/6w      
##  3 eta               1.289 SISR/6w              
##  4 eta               1.289 SSMIS_SF/6w          
##  5 eta               1.289 CES-D/6w             
##  6 eta               1.289 BHS/6w               
##  7 eta               1.215 Job_search/6 months  
##  8 eta               1.215 Help_seeking/6 months
##  9 eta               1.215 SISR/6 months        
## 10 eta               1.215 SSMIS_SF/6 months    
## 11 eta               1.215 CES-D/6 months       
## 12 eta               1.215 BHS/6 months

Sacks et al. (2011) (Quality-checked)

Extracting data from Table 4 (p. 1682)

# This study makes estimations based on two different analysis based on their
# propensity scores (what i read as the level of control) - i will keep them
# in the same tibble - "sample" indicates different propensity category. 
#Extracted from table 4 p. 1682 and only means plus standard deviation is taken.

Sacks2011 <- tibble(
  
  group = rep(c("Intervention",
                "Control"), 
              each = 1,10),
  
  
  outcome = rep(c(
#    "alcohol_intoxication",
#    "drug_use",
    "BDI_total",
    "GSI_total",
    "SF-36_mental_health",
    "SF-36_social_functioning",
#    "Health_rating",
    "SF-36_physical_health"
  ), each = 2,2),
  
  
  sample = rep(c("low/medium propensity", 
                 "high propensity"), each = 10,1),
  
  
  
  
  
  N = rep(c(42,34), 
          each = 10,1),
  
  m_pre = c(# Low/medium propensity 
  #          6.8, 6.5, # alcohol
  #          12.9, 17.8, # drugs
            16.1,  18.9, # BDI
            45.3, 47.7, # GSI
            42.4, 37.6, # SF-36 mental
            46.6, 38.5, # SF-36 social functioning 
  #          3.0, 3.0, # Health rating
            44.9, 47.2, # SF-36 physical health
            
            # High propensity
  #          6.3, 7.5, # alcohol
  #          15.9, 8.3, # drugs
            15.3, 15.3, # BDI
            42.0, 41.8, # GSI
            43.5, 43.1, # SF-36 mental
            13.2, 43.6, # SF-36 social functioning
  #          3.0, 3.5, # Health rating
            42.0, 42.6 # SF-36 physical health
            
            ),
  
  
  sd_pre = c(# Low/medium propensity 
  #           2.4, 2.2, # alcohol
  #           7.2, 8.7, # drugs
             6.7, 10.0, # BDI
             4.0, 9.0, # GSI# SF-36 mental
             11.8, 13.0, # SF-36 mental
             11.3, 13.7, # SF-36 social functioning
  #           1.2, 1.1, # Health rating
             9.0, 10.3, # SF-36 physical health
             
             # High propensity
  #           2.3, 0.7, # alcohol
  #           7.5, 5.7, # drugs
             3.7, 11.9, # BDI
             10.3, 7.4, # GSI
             10.4, 9.1, # SF-36 mental
             13.9, 11.3, # SF-36 social functioning
  #           1.0, 0.6, # Health rating
             10.4, 14.0), # SF-36 physical health
  
  
  m_post = c(
    # Low/medium propensity 
  #  2.7, 0.6, # alcohol
  #  0.6, 2.1, # drugs
    13.1, 8.2, # BDI
    38.6, 36.4, # GSI
    46.5, 47.2, # SF-36 mental
    44.9, 46.5, # SF-36 social functioning
  #  2.9, 3.0, # Health rating
    45.5, 47.6, # SF-36 physical health
    
    # High propensity
  #  0.4, 1.0, # alcohol
  #  0.4, 1.3, # drugs
    10.0, 16.5, # BDI
    39.0, 42.3, # GSI
    49.5, 41.6,  # SF-36 mental
    47.5, 46.3, # SF-36 social functioning
  #  3.1, 4.3, # Health rating
    42.8, 48.8 # SF-36 physical health
  ),
  
  sd_post = c(
    # Low/medium propensity 
  #  2.1, 1.8, # alcohol
  #  1.9, 4.7, # drugs
    11.9, 6.6, # BDI
    9.2, 10.1, # GSI
    14.2, 13.7, # SF-36 mental
    12.6, 11.5, # SF-36 social functioning
  #  1.2, 1.0, # Health rating
    7.3, 10.1,  # SF-36 physical health
    
    # High propensity
  #  1.1, 1.4, # alcohol# drugs
  #  0.9, 1.9, # drugs
    10.9, 8.4, # BDI
    7.7, 5.9, # GSI
    11.1, 13.2,  # SF-36 mental
    12.0, 7.7, # SF-36 social functioning
  #  1.0, 0.5, # Health rating
    12.9, 6.6 # SF-36 physical health
  )
  
); Sacks2011
## # A tibble: 20 × 8
##    group        outcome                  sample                    N m_pre
##    <chr>        <chr>                    <chr>                 <dbl> <dbl>
##  1 Intervention BDI_total                low/medium propensity    42  16.1
##  2 Control      BDI_total                low/medium propensity    42  18.9
##  3 Intervention GSI_total                low/medium propensity    42  45.3
##  4 Control      GSI_total                low/medium propensity    42  47.7
##  5 Intervention SF-36_mental_health      low/medium propensity    42  42.4
##  6 Control      SF-36_mental_health      low/medium propensity    42  37.6
##  7 Intervention SF-36_social_functioning low/medium propensity    42  46.6
##  8 Control      SF-36_social_functioning low/medium propensity    42  38.5
##  9 Intervention SF-36_physical_health    low/medium propensity    42  44.9
## 10 Control      SF-36_physical_health    low/medium propensity    42  47.2
## 11 Intervention BDI_total                high propensity          34  15.3
## 12 Control      BDI_total                high propensity          34  15.3
## 13 Intervention GSI_total                high propensity          34  42  
## 14 Control      GSI_total                high propensity          34  41.8
## 15 Intervention SF-36_mental_health      high propensity          34  43.5
## 16 Control      SF-36_mental_health      high propensity          34  43.1
## 17 Intervention SF-36_social_functioning high propensity          34  13.2
## 18 Control      SF-36_social_functioning high propensity          34  43.6
## 19 Intervention SF-36_physical_health    high propensity          34  42  
## 20 Control      SF-36_physical_health    high propensity          34  42.6
##    sd_pre m_post sd_post
##     <dbl>  <dbl>   <dbl>
##  1    6.7   13.1    11.9
##  2   10      8.2     6.6
##  3    4     38.6     9.2
##  4    9     36.4    10.1
##  5   11.8   46.5    14.2
##  6   13     47.2    13.7
##  7   11.3   44.9    12.6
##  8   13.7   46.5    11.5
##  9    9     45.5     7.3
## 10   10.3   47.6    10.1
## 11    3.7   10      10.9
## 12   11.9   16.5     8.4
## 13   10.3   39       7.7
## 14    7.4   42.3     5.9
## 15   10.4   49.5    11.1
## 16    9.1   41.6    13.2
## 17   13.9   47.5    12  
## 18   11.3   46.3     7.7
## 19   10.4   42.8    12.9
## 20   14     48.8     6.6
# Aggregate pretest measure

Sacks2011_prescores <- 
  Sacks2011 |> 
  select(-c(m_post:sd_post)) |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
  ) |>
  metafor::escalc(
    measure = "SMD", 
    m1i=m_pre_t, m2i=m_pre_c, 
    sd1i=sd_pre_t, sd2i=sd_pre_c, 
    n1i = N_t, n2i = N_c, 
    data = _
  ) 

Sack_pre_pooled <- 
  metafor::aggregate.escalc(Sacks2011_prescores, cluster = outcome, struct = "ID", addk = TRUE) |> 
  select(-c(yi:vi)) |> 
  mutate(sample = "low/medium propensity and high propensity sample")

# Aggregate posttest measure

Sacks2011_postscores <- 
  Sacks2011 |> 
  select(-c(m_pre:sd_pre)) |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
  ) |>
  metafor::escalc(
    measure = "SMD", 
    m1i=m_post_t, m2i=m_post_c, 
    sd1i=sd_post_t, sd2i=sd_post_c, 
    n1i = N_t, n2i = N_c, 
    data = _
  ) 

Sack_post_pooled <- 
  metafor::aggregate.escalc(Sacks2011_postscores, cluster = outcome, struct = "ID", addk = TRUE) |> 
  select(-c(yi:vi)) |> 
  mutate(sample = "low/medium propensity and high propensity sample")

Sack2011_pooled <- left_join(Sack_pre_pooled, Sack_post_pooled, by = join_by(outcome, sample, N_t, N_c, ki))


Sacks2011_est <- 
  Sack2011_pooled |> 
  mutate(

   analysis_plan = case_when(
     outcome == "BDI_total" ~ "All mental health outcomes/Depression",
      outcome == "GSI_total" ~ "All mental health outcomes",
      str_detect(outcome, "SF-36") ~ "Wellbeing and Quality of Life"),

   study = "Sacks et al. 2011",
  
   effect_size = "SMD",
   sd_used = "Pooled posttest SD",
   main_es_method = "Raw diff-in-diffs",
  
   # ppcor imputed
   ppcor = ppcor_imp,
   ppcor_method = "Imputed"


) |> 
  arrange(sample, outcome)|> 
  mutate(
    N_start_t = N_t,
    N_start_c = N_c,
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # For GSI and BDI lower scores are beneficial why these are reverted
    m_post = if_else(!outcome %in% c("BDI_total", "GSI_total"), m_post_t - m_post_c,
                     (m_post_t - m_post_c)*-1),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # For GSI and BDI lower scores are beneficial why these are reverted
    m_diff_t = if_else(!outcome %in% c("BDI_total", "GSI_total"), m_post_t - m_pre_t, (m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(!outcome %in% c("BDI_total", "GSI_total"), m_post_c - m_pre_c,(m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |>
  mutate(
         # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste0(outcome, "/", sample)
    
  ) |> 
  ungroup(); Sacks2011_est
## # A tibble: 5 × 66
##   outcome                  sample                                          
##   <chr>                    <chr>                                           
## 1 BDI_total                low/medium propensity and high propensity sample
## 2 GSI_total                low/medium propensity and high propensity sample
## 3 SF-36_mental_health      low/medium propensity and high propensity sample
## 4 SF-36_physical_health    low/medium propensity and high propensity sample
## 5 SF-36_social_functioning low/medium propensity and high propensity sample
##     N_t   N_c m_pre_t m_pre_c sd_pre_t sd_pre_c    ki m_post_t m_post_c
##   <dbl> <dbl>   <dbl>   <dbl>    <dbl>    <dbl> <int>    <dbl>    <dbl>
## 1    38    38   15.7    17.1      5.2     10.95     2    11.55    12.35
## 2    38    38   43.65   44.75     7.15     8.2      2    38.8     39.35
## 3    38    38   42.95   40.35    11.1     11.05     2    48       44.4 
## 4    38    38   43.45   44.9      9.7     12.15     2    44.15    48.2 
## 5    38    38   29.9    41.05    12.6     12.5      2    46.2     46.4 
##   sd_post_t sd_post_c analysis_plan                         study            
##       <dbl>     <dbl> <chr>                                 <chr>            
## 1     11.4       7.5  All mental health outcomes/Depression Sacks et al. 2011
## 2      8.45      8    All mental health outcomes            Sacks et al. 2011
## 3     12.65     13.45 Wellbeing and Quality of Life         Sacks et al. 2011
## 4     10.1       8.35 Wellbeing and Quality of Life         Sacks et al. 2011
## 5     12.3       9.6  Wellbeing and Quality of Life         Sacks et al. 2011
##   effect_size sd_used            main_es_method    ppcor ppcor_method N_start_t
##   <chr>       <chr>              <chr>             <dbl> <chr>            <dbl>
## 1 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed             38
## 2 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed             38
## 3 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed             38
## 4 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed             38
## 5 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed             38
##   N_start_c N_total df_ind  m_post sd_pool   d_post vd_post Wd_post      J
##       <dbl>   <dbl>  <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>
## 1        38      76     76  0.8000   9.649  0.08291 0.05268 0.05263 0.9901
## 2        38      76     76  0.5500   8.228  0.06684 0.05266 0.05263 0.9901
## 3        38      76     76  3.600   13.06   0.2757  0.05313 0.05263 0.9901
## 4        38      76     76 -4.050    9.266 -0.4371  0.05389 0.05263 0.9901
## 5        38      76     76 -0.2000  11.03  -0.01813 0.05263 0.05263 0.9901
##     g_post vg_post Wg_post m_diff_t m_diff_c     d_DD   vd_DD   Wd_DD     g_DD
##      <dbl>   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>
## 1  0.08209 0.05268 0.05263   4.15      4.75  -0.06218 0.05266 0.05263 -0.06157
## 2  0.06618 0.05266 0.05263   4.85      5.400 -0.06684 0.05266 0.05263 -0.06618
## 3  0.2730  0.05312 0.05263   5.050     4.050  0.07659 0.05267 0.05263  0.07583
## 4 -0.4327  0.05386 0.05263   0.7000    3.300 -0.2806  0.05315 0.05263 -0.2778 
## 5 -0.01795 0.05263 0.05263  16.3       5.35   0.9925  0.05911 0.05263  0.9827 
##     vg_DD   Wg_DD avg_cl_size avg_cl_type   icc icc_type n_covariates gamma_sqrt
##     <dbl>   <dbl>       <dbl> <chr>       <dbl> <chr>           <dbl>      <dbl>
## 1 0.05266 0.05263           8 Imputed       0.1 Imputed             1       0.97
## 2 0.05266 0.05263           8 Imputed       0.1 Imputed             1       0.97
## 3 0.05267 0.05263           8 Imputed       0.1 Imputed             1       0.97
## 4 0.05314 0.05263           8 Imputed       0.1 Imputed             1       0.97
## 5 0.05898 0.05263           8 Imputed       0.1 Imputed             1       0.97
##   df_adj  omega  gt_post vgt_post Wgt_post    gt_DD  vgt_DD  Wgt_DD    hg_DD
##    <dbl>  <dbl>    <dbl>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>
## 1  71.51 0.9895  0.07958  0.06847  0.06842 -0.05968 0.06845 0.06842 -0.02698
## 2  71.51 0.9895  0.06416  0.06845  0.06842 -0.06416 0.06845 0.06842 -0.02900
## 3  71.51 0.9895  0.2646   0.06891  0.06842  0.07351 0.06846 0.06842  0.03323
## 4  71.51 0.9895 -0.4195   0.06965  0.06842 -0.2693  0.06893 0.06842 -0.1216 
## 5  71.51 0.9895 -0.01740  0.06842  0.06842  0.9526  0.07477 0.06842  0.4243 
##    vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop
##     <dbl> <dbl> <dbl>           <dbl> <chr>             <dbl>     <dbl>
## 1 0.01398 71.51 71.51               1 eta                 1.3  -0.04808
## 2 0.01398 71.51 71.51               1 eta                 1.3  NA      
## 3 0.01398 71.51 71.51               1 eta                 1.3  NA      
## 4 0.01398 71.51 71.51               1 eta                 1.3  NA      
## 5 0.01398 71.51 71.51               1 eta                 1.3  NA      
##   vgt_DD_pop Wgt_DD_pop
##        <dbl>      <dbl>
## 1    0.06649    0.06648
## 2   NA         NA      
## 3   NA         NA      
## 4   NA         NA      
## 5   NA         NA      
##   vary_id                                                                  
##   <chr>                                                                    
## 1 BDI_total/low/medium propensity and high propensity sample               
## 2 GSI_total/low/medium propensity and high propensity sample               
## 3 SF-36_mental_health/low/medium propensity and high propensity sample     
## 4 SF-36_physical_health/low/medium propensity and high propensity sample   
## 5 SF-36_social_functioning/low/medium propensity and high propensity sample

Sajatovic et al. (2009) (Quality-checked)

Data was extracted from Table 2 (p. 1186)

# Data from Table 2 (p. 1186) provides mean and standard deviation. Table also 
# indcludes p-values indicating "Test of significance between the two groups 
# using repeated-measures analysis with PROC MIXED " (Sajatovic et al. 2009: 1186)


Sajatovic2009  <- tibble(
  group = rep(c("Life Goals Program", # Intervention
                "Treatment as usual"), # Control
              each = 1,9),
  
  outcome = rep(c("HAM-D", # Hamilton Depression Rating Scale
                   "YMRS", # Young Mania Rating Scale
                   "GAS"), # Global Assessment Scale
                 each = 2,3),
  
  timing = rep(c("3m", "6m", "12m"), each = 6),
  
    N_start = rep(c(
    83, 80,
    84, 80,
    83, 78
  ), each = 1, 3),
  
  N = c(
        # 3 months
        63, 65,
        63, 65,
        61, 61,
        
        # 6 months
        51, 55,
        51, 55,
        46, 53,
        
        # 12 months 
        41, 39, 
        41, 39, 
        40, 39
        ),
  
  
  
  m_pre = rep(c(
    19.98, 17.08, # HAM-D
    7.3, 7.58,    # YMRS
    56.53, 58.22  # GAS
    
  ), each =  1,3),
  
  
  sd_pre = rep(c(
    11.45, 10.99,
    5.41, 5.44,
    12.43, 12.00
    
  ), each = 1, 3),
  
  
  m_post = c(
    # 3 months
    16.30, 15.85,
    6.14, 8.02,
    60.10, 59.05,
    
    # 6 months
    16.35, 15.96,
    6.78, 7.69,
    61.72, 62.19, 
    
    # 12 months
    16.02, 14.39,
    5.85, 7.15,
    63.70, 64.51
  ), 
  
  
  sd_post = c(
    # 3 months
    9.68, 10.52,
    4.85, 5.38,
    11.63, 12.44, 
    
    # 6 months
    10.18, 12.47,
    5.36, 6.26,
    12.76, 14.42,
    
    # 12 months
    11.73, 10.87,
    4.74, 5.60,
    12.66, 15.90
  )
  
)


# Making the sajatovic2009 tibble wide in order to estimate the effect sizes and
# further analysis. 

# Turning data into wide format
    
Sajatovic2009_wide <- 
  Sajatovic2009 |>
  mutate(group = case_match(group, "Life Goals Program" ~ "t", "Treatment as usual" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  )



Sajatovic2009_est <- 
  Sajatovic2009_wide |> 
  mutate(
    # P-values obtained from table 2, calculated with repeated-measures analysis with 
    # PROC MIXED in SAS (p. 1186) - 
    # p_val = rep(c(0.40, 0.22, 0.97), each = 1,3), # Made across time measurements 
    
    analysis_plan = rep(
      c("All mental health outcomes/Depression", # HAM-D
        "All mental health outcomes", # YMRS
        "Social functioning (degree of impairment)" # GAS
      ), 3),
    
    study = "Sajatovic et al. 2009", 
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    
  ) |> 
  mutate(timing = factor(timing, levels = c("3m", "6m", "12m"))) |> 
 # arrange(outcome, timing)|> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # For YMRS and HAM-D lower scores are beneficial why these are reverted
    m_post = if_else(outcome != "GAS", (m_post_t - m_post_c)*-1,
                     m_post_t - m_post_c),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For YMRS and HAM-D lower scores are beneficial why these are reverted
    m_diff_t = if_else(outcome != "GAS", 
                       (m_post_t - m_pre_t)*-1,
                       m_post_t - m_pre_t),
    m_diff_c = if_else(outcome != "GAS",
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # "Group size was generally six to eight members" (p. 1183)
    avg_cl_size = 7, 
    avg_cl_type = "From study (p. 1183)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ), 
    
    vary_id = paste0(outcome, "/", timing)
    
  ) |> 
  ungroup(); Sajatovic2009_est
## # A tibble: 9 × 62
##   outcome timing N_start_t N_start_c   N_t   N_c m_pre_t m_pre_c sd_pre_t
##   <chr>   <fct>      <dbl>     <dbl> <dbl> <dbl>   <dbl>   <dbl>    <dbl>
## 1 HAM-D   3m            83        80    63    65   19.98   17.08    11.45
## 2 YMRS    3m            84        80    63    65    7.3     7.58     5.41
## 3 GAS     3m            83        78    61    61   56.53   58.22    12.43
## 4 HAM-D   6m            83        80    51    55   19.98   17.08    11.45
## 5 YMRS    6m            84        80    51    55    7.3     7.58     5.41
## 6 GAS     6m            83        78    46    53   56.53   58.22    12.43
## 7 HAM-D   12m           83        80    41    39   19.98   17.08    11.45
## 8 YMRS    12m           84        80    41    39    7.3     7.58     5.41
## 9 GAS     12m           83        78    40    39   56.53   58.22    12.43
##   sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
## 1    10.99    16.3     15.85      9.68     10.52
## 2     5.44     6.14     8.02      4.85      5.38
## 3    12       60.1     59.05     11.63     12.44
## 4    10.99    16.35    15.96     10.18     12.47
## 5     5.44     6.78     7.69      5.36      6.26
## 6    12       61.72    62.19     12.76     14.42
## 7    10.99    16.02    14.39     11.73     10.87
## 8     5.44     5.85     7.15      4.74      5.6 
## 9    12       63.7     64.51     12.66     15.9 
##   analysis_plan                             study                 effect_size
##   <chr>                                     <chr>                 <chr>      
## 1 All mental health outcomes/Depression     Sajatovic et al. 2009 SMD        
## 2 All mental health outcomes                Sajatovic et al. 2009 SMD        
## 3 Social functioning (degree of impairment) Sajatovic et al. 2009 SMD        
## 4 All mental health outcomes/Depression     Sajatovic et al. 2009 SMD        
## 5 All mental health outcomes                Sajatovic et al. 2009 SMD        
## 6 Social functioning (degree of impairment) Sajatovic et al. 2009 SMD        
## 7 All mental health outcomes/Depression     Sajatovic et al. 2009 SMD        
## 8 All mental health outcomes                Sajatovic et al. 2009 SMD        
## 9 Social functioning (degree of impairment) Sajatovic et al. 2009 SMD        
##   sd_used            main_es_method    ppcor ppcor_method N_total df_ind  m_post
##   <chr>              <chr>             <dbl> <chr>          <dbl>  <dbl>   <dbl>
## 1 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          128    128 -0.4500
## 2 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          128    128  1.88  
## 3 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          122    122  1.050 
## 4 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          106    106 -0.3900
## 5 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          106    106  0.91  
## 6 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           99     99 -0.4700
## 7 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           80     80 -1.630 
## 8 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           80     80  1.3   
## 9 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           79     79 -0.8100
##   sd_pool   d_post vd_post Wd_post      J   g_post vg_post Wg_post m_diff_t
##     <dbl>    <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>   <dbl>    <dbl>
## 1  10.12  -0.04449 0.03127 0.03126 0.9941 -0.04423 0.03127 0.03126   3.68  
## 2   5.126  0.3668  0.03178 0.03126 0.9941  0.3646  0.03178 0.03126   1.16  
## 3  12.04   0.08720 0.03282 0.03279 0.9938  0.08666 0.03282 0.03279   3.57  
## 4  11.43  -0.03413 0.03780 0.03779 0.9929 -0.03389 0.03780 0.03779   3.63  
## 5   5.845  0.1557  0.03790 0.03779 0.9929  0.1546  0.03790 0.03779   0.5200
## 6  13.67  -0.03437 0.04061 0.04061 0.9924 -0.03411 0.04061 0.04061   5.19  
## 7  11.32  -0.1440  0.05016 0.05003 0.9906 -0.1426  0.05016 0.05003   3.96  
## 8   5.177  0.2511  0.05043 0.05003 0.9906  0.2488  0.05042 0.05003   1.45  
## 9  14.35  -0.05644 0.05066 0.05064 0.9905 -0.05591 0.05066 0.05064   7.17  
##   m_diff_c    d_DD   vd_DD   Wd_DD    g_DD   vg_DD   Wg_DD avg_cl_size
##      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>       <dbl>
## 1   1.230  0.2422  0.03149 0.03126 0.2408  0.03148 0.03126           7
## 2  -0.4400 0.3121  0.03164 0.03126 0.3103  0.03163 0.03126           7
## 3   0.8300 0.2275  0.03300 0.03279 0.2261  0.03300 0.03279           7
## 4   1.120  0.2197  0.03802 0.03779 0.2181  0.03801 0.03779           7
## 5  -0.1100 0.1078  0.03784 0.03779 0.1070  0.03784 0.03779           7
## 6   3.97   0.08921 0.04065 0.04061 0.08854 0.04065 0.04061           7
## 7   2.690  0.1122  0.05011 0.05003 0.1111  0.05011 0.05003           7
## 8   0.4300 0.1970  0.05027 0.05003 0.1952  0.05027 0.05003           7
## 9   6.290  0.06132 0.05066 0.05064 0.06074 0.05066 0.05064           7
##   avg_cl_type            icc icc_type n_covariates gamma_sqrt df_adj  omega
##   <chr>                <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
## 1 From study (p. 1183)   0.1 Imputed             1      0.972 122.0  0.9938
## 2 From study (p. 1183)   0.1 Imputed             1      0.972 122.0  0.9938
## 3 From study (p. 1183)   0.1 Imputed             1      0.972 116.2  0.9935
## 4 From study (p. 1183)   0.1 Imputed             1      0.971 100.8  0.9925
## 5 From study (p. 1183)   0.1 Imputed             1      0.971 100.8  0.9925
## 6 From study (p. 1183)   0.1 Imputed             1      0.97   94.17 0.9920
## 7 From study (p. 1183)   0.1 Imputed             1      0.971  75.57 0.9900
## 8 From study (p. 1183)   0.1 Imputed             1      0.971  75.57 0.9900
## 9 From study (p. 1183)   0.1 Imputed             1      0.971  74.64 0.9899
##    gt_post vgt_post Wgt_post   gt_DD  vgt_DD  Wgt_DD   hg_DD   vhg_DD   h_DD
##      <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>    <dbl>  <dbl>
## 1 -0.04297  0.03924  0.03923 0.2340  0.03945 0.03923 0.1068  0.008196 122.0 
## 2  0.3543   0.03974  0.03923 0.3015  0.03960 0.03923 0.1376  0.008196 122.0 
## 3  0.08421  0.04101  0.04098 0.2197  0.04119 0.04098 0.1006  0.008608 116.2 
## 4 -0.03289  0.04773  0.04773 0.2117  0.04795 0.04773 0.09643 0.009917 100.8 
## 5  0.1501   0.04784  0.04773 0.1039  0.04778 0.04773 0.04734 0.009917 100.8 
## 6 -0.03307  0.05178  0.05177 0.08585 0.05181 0.05177 0.03887 0.01062   94.17
## 7 -0.1384   0.06222  0.06209 0.1079  0.06217 0.06209 0.04978 0.01323   75.57
## 8  0.2414   0.06247  0.06209 0.1894  0.06233 0.06209 0.08739 0.01323   75.57
## 9 -0.05425  0.06312  0.06310 0.05894 0.06312 0.06310 0.02716 0.01340   74.64
##    df_DD n_covariates_DD adj_fct_DD adj_value_DD vary_id  
##    <dbl>           <dbl> <chr>             <dbl> <chr>    
## 1 122.0                1 eta               1.255 HAM-D/3m 
## 2 122.0                1 eta               1.255 YMRS/3m  
## 3 116.2                1 eta               1.25  GAS/3m   
## 4 100.8                1 eta               1.263 HAM-D/6m 
## 5 100.8                1 eta               1.263 YMRS/6m  
## 6  94.17               1 eta               1.275 GAS/6m   
## 7  75.57               1 eta               1.241 HAM-D/12m
## 8  75.57               1 eta               1.241 YMRS/12m 
## 9  74.64               1 eta               1.246 GAS/12m

Saloheimo et al. (2016) (Quality-checked)

Sample size data was extracted from page 5. Effect size data was retrieved from Table 1 and text reported results (p. 6)

Saloheimo2016_est <- 
  tibble(
    
    study = "Saloheimo 2016",
    outcome = "HAM-D",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    timing = "3m",
    
    N_start_t = 42,
    N_start_c = 46,
    
    N_t = 27,
    N_c = 34,
    
    N_total = N_t + N_c, 
    # I cant find these results anywhere (JKJ)
    m_pre_t = 19.3, 
    m_pre_c = 18.9,
    
    sd_pre_t = 3.8,
    sd_pre_c = 3.7,
    
    m_post_t = 8.3, 
    m_post_c = 11.1,
    
    sd_post_t = 6.3,
    sd_post_c = 6.4
    
    
  ) |> 
  mutate(
    
    analysis_plan = "All mental health outcomes/Depression",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    df_ind = N_total,
    
    #  HAM-D lower scores are beneficial, thus these are reverted
    m_post = (m_post_t - m_post_c)*-1,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For YMRS and HAM-D lower scores are beneficial why these are reverted
    m_diff_t =  (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # "Therapists act as coaches for groups of 4-6 patients" (p. 3)
    avg_cl_size = 3.47, 
    avg_cl_type = "From study (p. 4)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ), 
    
    vary_id = paste0(outcome, " (one es only)")
    
  ) |> 
  ungroup(); Saloheimo2016_est
## # A tibble: 1 × 62
##   study          outcome effect_size sd_used             main_es_method   
##   <chr>          <chr>   <chr>       <chr>               <chr>            
## 1 Saloheimo 2016 HAM-D   SMD         Pooled posttest SDs Raw diff-in-diffs
##   timing N_start_t N_start_c   N_t   N_c N_total m_pre_t m_pre_c sd_pre_t
##   <chr>      <dbl>     <dbl> <dbl> <dbl>   <dbl>   <dbl>   <dbl>    <dbl>
## 1 3m            42        46    27    34      61    19.3    18.9      3.8
##   sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
## 1      3.7      8.3     11.1       6.3       6.4
##   analysis_plan                         ppcor ppcor_method df_ind m_post sd_pool
##   <chr>                                 <dbl> <chr>         <dbl>  <dbl>   <dbl>
## 1 All mental health outcomes/Depression   0.5 Imputed          61    2.8   6.356
##   d_post vd_post Wd_post      J g_post vg_post Wg_post m_diff_t m_diff_c   d_DD
##    <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>  <dbl>
## 1 0.4405 0.06804 0.06645 0.9877 0.4351 0.06800 0.06645       11      7.8 0.5035
##     vd_DD   Wd_DD   g_DD   vg_DD   Wg_DD avg_cl_size avg_cl_type         icc
##     <dbl>   <dbl>  <dbl>   <dbl>   <dbl>       <dbl> <chr>             <dbl>
## 1 0.06853 0.06645 0.4972 0.06848 0.06645        3.47 From study (p. 4)   0.1
##   icc_type n_covariates gamma_sqrt df_adj  omega gt_post vgt_post Wgt_post
##   <chr>           <dbl>      <dbl>  <dbl>  <dbl>   <dbl>    <dbl>    <dbl>
## 1 Imputed             1      0.969  58.19 0.9871  0.4213  0.07415  0.07263
##    gt_DD  vgt_DD  Wgt_DD  hg_DD  vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD
##    <dbl>   <dbl>   <dbl>  <dbl>   <dbl> <dbl> <dbl>           <dbl> <chr>     
## 1 0.4815 0.07462 0.07263 0.2332 0.01719 58.19 58.19               1 eta       
##   adj_value_DD vary_id            
##          <dbl> <chr>              
## 1        1.093 HAM-D (one es only)

Schrank et al. (2016) (Quality-checked)

Data was extracted from Tables 1, 2, and 3 in the same article (p. 240-242)

# Based on table 1, 2 and 3 in the same article 
Schrank2016 <- 
  tibble(
    group = rep(c("Intervention", "Control"), each = 1, 6),
    
    outcome = rep(c(
      "WEMWBS", 
      "MANSA", 
      # "PPI", 
      "BPRS",
      #   "SDHS",
      "IHS",
      "RSES", 
      "RES"
    ), each = 2,1),
    
    N_start = 47,
    
    N = rep(c(
      43, 41
    ), each = 1, 6),
    
    m_pre = c(
      3.19, 3.00,
      4.05, 4.14,
      #3.58, 3.44,
      30.70, 33.57,
      #2.29, 2.48,
      4.02, 3.72, 
      2.24, 2.09,
      2.74, 2.71
      ),
    
    sd_pre = c(
      0.76, 0.89,
      0.85, 1.01,
      #  0.73, 0.80,
      8.81, 8.42,
      #  0.69, 0.76,
      0.79, 0.85,
      0.64, 0.66,
      0.32, 0.32
    ),
    
    m_post = c(
      3.36,3.24,
      4.42, 4.21,
      # 3.72, 3.48,
      29.37, 33.23,
      #  2.13, 2.34,
      4.11, 4.04,
      2.37, 2.21,
      2.8,2.73
    ),
    
    se_post = c(
      0.10, 0.10,
      0.10, 0.10,
      #  0.07, 0.07,
      0.96, 0.98,
      #  0.08, 0.09,
      0.1, 0.1,
      0.07, 0.07,
      0.04,0.04) 
  ) |> 
  mutate(sd_post = se_post * sqrt(N)) # Calculating the standard deviation from the standard error




Schrank2016_wide <-
  Schrank2016 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  )



Schrank2016_est <-           
  Schrank2016_wide |>
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "WEMWBS|MANSA") ~ "Wellbeing and Quality of Life",
      str_detect(outcome, "BPRS") ~ "All mental health outcomes",
      str_detect(outcome, "IHS|RSES|RES") ~ "Hope, Empowerment & Self-efficacy",
      .default = NA_character_
    ),
    
    # ANCOVA results from table 3, p. 242
    F_val = c(
      0.8,
      2.3,
      #  5.9,
      7.8,
      #   3.0,
      3.0,
      2.9,
      2.0
    ),
    
    df1 = rep(c(1)),
    df2 = rep(c(81)),
    
    p_val_f = c(
      0.15,
      0.21,
      #    0.30,
      0.42,
      #    0.29,
      0.08,
      0.23,
      0.22
    )
    
  ) |> 
  mutate(
    study = "Schrank et al. 2016",
    
   effect_size = "SMD",
   sd_used = "Pooled posttest SD",
   main_es_method = "Raw diff-in-diffs",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    ppcor_method = "Not computable - F value used for variance estimation",
    
    # The outcome BPRS is reverted because lower score is beneficial
    m_post = if_else(outcome != "BPRS", 
                     m_post_t - m_post_c,
                     (m_post_t - m_post_c) * -1),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # The outcome BPRS is reverted because lower score is beneficial
    m_diff_t = if_else(outcome != "BPRS", 
                       m_post_t - m_pre_t,
                       (m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(outcome != "BPRS",
                       m_post_c - m_pre_c,
                       (m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = d_DD^2/F_val + d_DD^2/(2*df_ind),
    Wd_DD = d_DD^2/F_val,
    
    g_DD = J * d_DD, 
    vg_DD = g_DD^2/F_val + g_DD^2/(2*df_ind),
    Wg_DD = g_DD^2/F_val
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # "WELLFOCUS PPT was provided to six groups, and each group had an average of eight" (p. 238)
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 238)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      F_val = F_val,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    vary_id = outcome
  ) |> 
  ungroup(); Schrank2016_est
## # A tibble: 6 × 66
##   outcome N_start_t N_start_c   N_t   N_c m_pre_t m_pre_c sd_pre_t sd_pre_c
##   <chr>       <dbl>     <dbl> <dbl> <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 WEMWBS         47        47    43    41    3.19    3        0.76     0.89
## 2 MANSA          47        47    43    41    4.05    4.14     0.85     1.01
## 3 BPRS           47        47    43    41   30.7    33.57     8.81     8.42
## 4 IHS            47        47    43    41    4.02    3.72     0.79     0.85
## 5 RSES           47        47    43    41    2.24    2.09     0.64     0.66
## 6 RES            47        47    43    41    2.74    2.71     0.32     0.32
##   m_post_t m_post_c se_post_t se_post_c sd_post_t sd_post_c
##      <dbl>    <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
## 1     3.36     3.24      0.1       0.1     0.6557    0.6403
## 2     4.42     4.21      0.1       0.1     0.6557    0.6403
## 3    29.37    33.23      0.96      0.98    6.295     6.275 
## 4     4.11     4.04      0.1       0.1     0.6557    0.6403
## 5     2.37     2.21      0.07      0.07    0.4590    0.4482
## 6     2.8      2.73      0.04      0.04    0.2623    0.2561
##   analysis_plan                     F_val   df1   df2 p_val_f
##   <chr>                             <dbl> <dbl> <dbl>   <dbl>
## 1 Wellbeing and Quality of Life       0.8     1    81    0.15
## 2 Wellbeing and Quality of Life       2.3     1    81    0.21
## 3 All mental health outcomes          7.8     1    81    0.42
## 4 Hope, Empowerment & Self-efficacy   3       1    81    0.08
## 5 Hope, Empowerment & Self-efficacy   2.9     1    81    0.23
## 6 Hope, Empowerment & Self-efficacy   2       1    81    0.22
##   study               effect_size sd_used            main_es_method    N_total
##   <chr>               <chr>       <chr>              <chr>               <dbl>
## 1 Schrank et al. 2016 SMD         Pooled posttest SD Raw diff-in-diffs      84
## 2 Schrank et al. 2016 SMD         Pooled posttest SD Raw diff-in-diffs      84
## 3 Schrank et al. 2016 SMD         Pooled posttest SD Raw diff-in-diffs      84
## 4 Schrank et al. 2016 SMD         Pooled posttest SD Raw diff-in-diffs      84
## 5 Schrank et al. 2016 SMD         Pooled posttest SD Raw diff-in-diffs      84
## 6 Schrank et al. 2016 SMD         Pooled posttest SD Raw diff-in-diffs      84
##   df_ind ppcor_method                                           m_post sd_pool
##    <dbl> <chr>                                                   <dbl>   <dbl>
## 1     84 Not computable - F value used for variance estimation 0.1200   0.6483
## 2     84 Not computable - F value used for variance estimation 0.21     0.6483
## 3     84 Not computable - F value used for variance estimation 3.860    6.285 
## 4     84 Not computable - F value used for variance estimation 0.07000  0.6483
## 5     84 Not computable - F value used for variance estimation 0.1600   0.4538
## 6     84 Not computable - F value used for variance estimation 0.07000  0.2593
##   d_post vd_post Wd_post      J g_post vg_post Wg_post m_diff_t m_diff_c
##    <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 0.1851 0.04785 0.04765 0.9910 0.1835 0.04785 0.04765  0.17     0.2400 
## 2 0.3239 0.04827 0.04765 0.9910 0.3210 0.04826 0.04765  0.37     0.07000
## 3 0.6141 0.04989 0.04765 0.9910 0.6086 0.04985 0.04765  1.330    0.3400 
## 4 0.1080 0.04772 0.04765 0.9910 0.1070 0.04771 0.04765  0.09000  0.3200 
## 5 0.3526 0.04839 0.04765 0.9910 0.3494 0.04837 0.04765  0.1300   0.1200 
## 6 0.2700 0.04808 0.04765 0.9910 0.2675 0.04807 0.04765  0.06000  0.02000
##       d_DD     vd_DD     Wd_DD     g_DD     vg_DD     Wg_DD avg_cl_size
##      <dbl>     <dbl>     <dbl>    <dbl>     <dbl>     <dbl>       <dbl>
## 1 -0.1080  0.01464   0.01457   -0.1070  0.01438   0.01431             8
## 2  0.4628  0.09439   0.09311    0.4586  0.09271   0.09145             8
## 3  0.1575  0.003328  0.003181   0.1561  0.003269  0.003124            8
## 4 -0.3548  0.04271   0.04196   -0.3516  0.04195   0.04121             8
## 5  0.02204 0.0001703 0.0001675  0.02184 0.0001673 0.0001645           8
## 6  0.1543  0.01204   0.01190    0.1529  0.01182   0.01169             8
##   avg_cl_type           icc icc_type n_covariates gamma_sqrt df_adj  omega
##   <chr>               <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
## 1 From study (p. 238)   0.1 Imputed             1      0.971  79.12 0.9905
## 2 From study (p. 238)   0.1 Imputed             1      0.971  79.12 0.9905
## 3 From study (p. 238)   0.1 Imputed             1      0.971  79.12 0.9905
## 4 From study (p. 238)   0.1 Imputed             1      0.971  79.12 0.9905
## 5 From study (p. 238)   0.1 Imputed             1      0.971  79.12 0.9905
## 6 From study (p. 238)   0.1 Imputed             1      0.971  79.12 0.9905
##   gt_post vgt_post Wgt_post    gt_DD    vgt_DD    Wgt_DD    hg_DD  vhg_DD  h_DD
##     <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl>   <dbl> <dbl>
## 1  0.1780  0.06166  0.06146 -0.1039  0.01746   0.01739   -0.08848 0.01264 79.12
## 2  0.3116  0.06208  0.06146  0.4451  0.1124    0.1111     0.1498  0.01264 79.12
## 3  0.5906  0.06367  0.06146  0.1515  0.003940  0.003795   0.2747  0.01264 79.12
## 4  0.1039  0.06153  0.06146 -0.3412  0.05080   0.05007   -0.1710  0.01264 79.12
## 5  0.3391  0.06219  0.06146  0.02119 0.0002027 0.0001998  0.1682  0.01264 79.12
## 6  0.2596  0.06189  0.06146  0.1484  0.01434   0.01420    0.1398  0.01264 79.12
##   df_DD n_covariates_DD adj_fct_DD adj_value_DD vary_id
##   <dbl>           <dbl> <chr>             <dbl> <chr>  
## 1 79.12               1 eta                1.29 WEMWBS 
## 2 79.12               1 eta                1.29 MANSA  
## 3 79.12               1 eta                1.29 BPRS   
## 4 79.12               1 eta                1.29 IHS    
## 5 79.12               1 eta                1.29 RSES   
## 6 79.12               1 eta                1.29 RES

Schäfer et al. (2019) (Quality-checked)

Entering data from table 2 (p. 9)

# Taken from table 2, p. 9 
schafer2019 <- tibble(
  group = as.factor(rep(c("Seeking Safety",
                          "Relapse Prevention Training",
                          "TAU"),
                        each = 1, 21)),
  
  outcome = (rep(c("PSS-I", 
                   "PDS", 
                   "Drug and alcohol free days",
                   "ASI alcohol severity",
                   "ASI drug severity",
                   "BDI-II",
                   "DERS"),
                 each = 3,3)),
  
  timing = rep(c("Post", "3m", "6m"), each = 21),
  
  N_start = rep(c(111, 115, 117), each = 1, 21),

  N = rep(c(111, 115, 117), each = 1, 21),
  
  m_pre = rep(c(
    25.4, 27.5, 28.9,
    25.7, 27.6, 27.7,
    16.6, 15.6, 15.6,
    .28, .33, .32,
    .09, .1, .09,
    25.3, 29.4, 28.6,
    100.5, 110.7, 111.9),
    each = 1,3), 
  
  sd_pre = rep(c(
    9.7, 9.8, 9.4,
    11.2, 10., 10.2,
    12.1, 11.9, 12.3,
    .25, .29, .27,
    .11, .14, .11,
    13.2, 11.7, 10.9,
    26.9, 24.6, 25.3),
    each = 1,3), 
  
  
  
  m_post = c(
    # post mean values
    
    22.9, 24.3, 26.1,
    20.8, 23., 24.,
    18.3, 19.4, 16.3,
    .22, .25, .3,
    .07, .06, .08,
    19., 26.2, 25.8,
    95.6, 102.9, 109.1, 
    
    
    # 3m follow-up
    22.1, 23.7, 24.5,
    20.9, 22.5, 24.3,
    19.5, 21.1, 17.6,
    0.2, 0.25, 0.28,
    .06, .06, .06,
    19.9, 25., 25.2,
    94.1, 100.9, 106.8,
    
    #6m follow-up
    22.1, 20.7, 24.3,
    19.4, 19.9, 23.7,
    20.5, 22.4, 16.4,
    .24, .19, .27,
    .05, .04, .07,
    18.5, 22.3, 24.1,
    92.7, 100., 107.3),
  
  sd_post = c(
    # post sd values
    12.4, 11.9, 10.3,
    12.0, 11.2, 10.7,
    11.9, 11.7, 12.4,
    .24, .24, .28,
    .12, .11, .11,
    12.4, 12., 12.6,
    24.7, 26.4, 24.5,
    
    # 3m sd values
    12.2, 11.5, 11.8,
    13.8, 11.5, 12.5,
    11.6, 10.2, 11.9,
    .23, .26, .28,
    .11, .12, .10,
    14.4, 14., 13.0,
    27.2, 27.8, 26.1,
    
    # 6m sd values
    11.5, 11., 11.4,
    11.9, 11.7, 12.5,
    11.3, 10.7, 12.7,
    .26, .22, .28,
    .10, .10, .11,
    12.5, 13.3, 14.0,
    24.7, 25.2, 25.4
  )
)


# Effect sizes can be seen in table 2, p. 9
schafer2019_Relapse_wide <- 
  schafer2019 |> 
  filter(!str_detect(group, "Seeking")) |> 
  mutate(group = case_match(group, "Relapse Prevention Training" ~ "t", "TAU" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  ) |> 
  mutate(
    treatment = "Relapse Prevention Training"
  ) |> 
  relocate(treatment)

schafer2019_Seeking_wide <- 
  schafer2019 |> 
  filter(!str_detect(group, "Relapse")) |> 
  mutate(group = case_match(group, "Seeking Safety" ~ "t", "TAU" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  ) |> 
  mutate(
    treatment = "Seeking Safety"
  ) |> 
  relocate(treatment)



schafer2019_est <- 
  bind_rows(schafer2019_Relapse_wide, schafer2019_Seeking_wide) |> 
  arrange(outcome, timing) |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "ASI|Drug and alcohol free days") ~ "Alcohol and drug abuse/misuse",
      str_detect(outcome, "BDI-II") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "DERS") ~ "Unused outcomes",
      str_detect(outcome, "PDS|PSS-I") ~ "All mental health outcomes",
      .default = NA_character_
    ),
    
    study = "Schäfer et al. 2019",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor is imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    ) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    m_post = (m_post_t - m_post_c) * -1, 
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For all scores: Lower is beneficial
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # Group size was bup to 8 (p. 4)
    
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 4)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
      
    vary_id = paste(outcome, timing, treatment, sep = "/")
  ) |> 
  ungroup(); schafer2019_est
## # A tibble: 42 × 66
##    treatment                   outcome                    timing N_start_t
##    <chr>                       <chr>                      <chr>      <dbl>
##  1 Relapse Prevention Training ASI alcohol severity       3m           115
##  2 Seeking Safety              ASI alcohol severity       3m           111
##  3 Relapse Prevention Training ASI alcohol severity       6m           115
##  4 Seeking Safety              ASI alcohol severity       6m           111
##  5 Relapse Prevention Training ASI alcohol severity       Post         115
##  6 Seeking Safety              ASI alcohol severity       Post         111
##  7 Relapse Prevention Training ASI drug severity          3m           115
##  8 Seeking Safety              ASI drug severity          3m           111
##  9 Relapse Prevention Training ASI drug severity          6m           115
## 10 Seeking Safety              ASI drug severity          6m           111
## 11 Relapse Prevention Training ASI drug severity          Post         115
## 12 Seeking Safety              ASI drug severity          Post         111
## 13 Relapse Prevention Training BDI-II                     3m           115
## 14 Seeking Safety              BDI-II                     3m           111
## 15 Relapse Prevention Training BDI-II                     6m           115
## 16 Seeking Safety              BDI-II                     6m           111
## 17 Relapse Prevention Training BDI-II                     Post         115
## 18 Seeking Safety              BDI-II                     Post         111
## 19 Relapse Prevention Training DERS                       3m           115
## 20 Seeking Safety              DERS                       3m           111
## 21 Relapse Prevention Training DERS                       6m           115
## 22 Seeking Safety              DERS                       6m           111
## 23 Relapse Prevention Training DERS                       Post         115
## 24 Seeking Safety              DERS                       Post         111
## 25 Relapse Prevention Training Drug and alcohol free days 3m           115
## 26 Seeking Safety              Drug and alcohol free days 3m           111
## 27 Relapse Prevention Training Drug and alcohol free days 6m           115
## 28 Seeking Safety              Drug and alcohol free days 6m           111
## 29 Relapse Prevention Training Drug and alcohol free days Post         115
## 30 Seeking Safety              Drug and alcohol free days Post         111
## 31 Relapse Prevention Training PDS                        3m           115
## 32 Seeking Safety              PDS                        3m           111
## 33 Relapse Prevention Training PDS                        6m           115
## 34 Seeking Safety              PDS                        6m           111
## 35 Relapse Prevention Training PDS                        Post         115
## 36 Seeking Safety              PDS                        Post         111
## 37 Relapse Prevention Training PSS-I                      3m           115
## 38 Seeking Safety              PSS-I                      3m           111
## 39 Relapse Prevention Training PSS-I                      6m           115
## 40 Seeking Safety              PSS-I                      6m           111
## 41 Relapse Prevention Training PSS-I                      Post         115
## 42 Seeking Safety              PSS-I                      Post         111
##    N_start_c   N_t   N_c m_pre_t m_pre_c sd_pre_t sd_pre_c m_post_t m_post_c
##        <dbl> <dbl> <dbl>   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
##  1       117   115   117    0.33    0.32     0.29     0.27     0.25     0.28
##  2       117   111   117    0.28    0.32     0.25     0.27     0.2      0.28
##  3       117   115   117    0.33    0.32     0.29     0.27     0.19     0.27
##  4       117   111   117    0.28    0.32     0.25     0.27     0.24     0.27
##  5       117   115   117    0.33    0.32     0.29     0.27     0.25     0.3 
##  6       117   111   117    0.28    0.32     0.25     0.27     0.22     0.3 
##  7       117   115   117    0.1     0.09     0.14     0.11     0.06     0.06
##  8       117   111   117    0.09    0.09     0.11     0.11     0.06     0.06
##  9       117   115   117    0.1     0.09     0.14     0.11     0.04     0.07
## 10       117   111   117    0.09    0.09     0.11     0.11     0.05     0.07
## 11       117   115   117    0.1     0.09     0.14     0.11     0.06     0.08
## 12       117   111   117    0.09    0.09     0.11     0.11     0.07     0.08
## 13       117   115   117   29.4    28.6     11.7     10.9     25       25.2 
## 14       117   111   117   25.3    28.6     13.2     10.9     19.9     25.2 
## 15       117   115   117   29.4    28.6     11.7     10.9     22.3     24.1 
## 16       117   111   117   25.3    28.6     13.2     10.9     18.5     24.1 
## 17       117   115   117   29.4    28.6     11.7     10.9     26.2     25.8 
## 18       117   111   117   25.3    28.6     13.2     10.9     19       25.8 
## 19       117   115   117  110.7   111.9     24.6     25.3    100.9    106.8 
## 20       117   111   117  100.5   111.9     26.9     25.3     94.1    106.8 
## 21       117   115   117  110.7   111.9     24.6     25.3    100      107.3 
## 22       117   111   117  100.5   111.9     26.9     25.3     92.7    107.3 
## 23       117   115   117  110.7   111.9     24.6     25.3    102.9    109.1 
## 24       117   111   117  100.5   111.9     26.9     25.3     95.6    109.1 
## 25       117   115   117   15.6    15.6     11.9     12.3     21.1     17.6 
## 26       117   111   117   16.6    15.6     12.1     12.3     19.5     17.6 
## 27       117   115   117   15.6    15.6     11.9     12.3     22.4     16.4 
## 28       117   111   117   16.6    15.6     12.1     12.3     20.5     16.4 
## 29       117   115   117   15.6    15.6     11.9     12.3     19.4     16.3 
## 30       117   111   117   16.6    15.6     12.1     12.3     18.3     16.3 
## 31       117   115   117   27.6    27.7     10       10.2     22.5     24.3 
## 32       117   111   117   25.7    27.7     11.2     10.2     20.9     24.3 
## 33       117   115   117   27.6    27.7     10       10.2     19.9     23.7 
## 34       117   111   117   25.7    27.7     11.2     10.2     19.4     23.7 
## 35       117   115   117   27.6    27.7     10       10.2     23       24   
## 36       117   111   117   25.7    27.7     11.2     10.2     20.8     24   
## 37       117   115   117   27.5    28.9      9.8      9.4     23.7     24.5 
## 38       117   111   117   25.4    28.9      9.7      9.4     22.1     24.5 
## 39       117   115   117   27.5    28.9      9.8      9.4     20.7     24.3 
## 40       117   111   117   25.4    28.9      9.7      9.4     22.1     24.3 
## 41       117   115   117   27.5    28.9      9.8      9.4     24.3     26.1 
## 42       117   111   117   25.4    28.9      9.7      9.4     22.9     26.1 
##    sd_post_t sd_post_c analysis_plan                         study              
##        <dbl>     <dbl> <chr>                                 <chr>              
##  1      0.26      0.28 Alcohol and drug abuse/misuse         Schäfer et al. 2019
##  2      0.23      0.28 Alcohol and drug abuse/misuse         Schäfer et al. 2019
##  3      0.22      0.28 Alcohol and drug abuse/misuse         Schäfer et al. 2019
##  4      0.26      0.28 Alcohol and drug abuse/misuse         Schäfer et al. 2019
##  5      0.24      0.28 Alcohol and drug abuse/misuse         Schäfer et al. 2019
##  6      0.24      0.28 Alcohol and drug abuse/misuse         Schäfer et al. 2019
##  7      0.12      0.1  Alcohol and drug abuse/misuse         Schäfer et al. 2019
##  8      0.11      0.1  Alcohol and drug abuse/misuse         Schäfer et al. 2019
##  9      0.1       0.11 Alcohol and drug abuse/misuse         Schäfer et al. 2019
## 10      0.1       0.11 Alcohol and drug abuse/misuse         Schäfer et al. 2019
## 11      0.11      0.11 Alcohol and drug abuse/misuse         Schäfer et al. 2019
## 12      0.12      0.11 Alcohol and drug abuse/misuse         Schäfer et al. 2019
## 13     14        13    All mental health outcomes/Depression Schäfer et al. 2019
## 14     14.4      13    All mental health outcomes/Depression Schäfer et al. 2019
## 15     13.3      14    All mental health outcomes/Depression Schäfer et al. 2019
## 16     12.5      14    All mental health outcomes/Depression Schäfer et al. 2019
## 17     12        12.6  All mental health outcomes/Depression Schäfer et al. 2019
## 18     12.4      12.6  All mental health outcomes/Depression Schäfer et al. 2019
## 19     27.8      26.1  Unused outcomes                       Schäfer et al. 2019
## 20     27.2      26.1  Unused outcomes                       Schäfer et al. 2019
## 21     25.2      25.4  Unused outcomes                       Schäfer et al. 2019
## 22     24.7      25.4  Unused outcomes                       Schäfer et al. 2019
## 23     26.4      24.5  Unused outcomes                       Schäfer et al. 2019
## 24     24.7      24.5  Unused outcomes                       Schäfer et al. 2019
## 25     10.2      11.9  Alcohol and drug abuse/misuse         Schäfer et al. 2019
## 26     11.6      11.9  Alcohol and drug abuse/misuse         Schäfer et al. 2019
## 27     10.7      12.7  Alcohol and drug abuse/misuse         Schäfer et al. 2019
## 28     11.3      12.7  Alcohol and drug abuse/misuse         Schäfer et al. 2019
## 29     11.7      12.4  Alcohol and drug abuse/misuse         Schäfer et al. 2019
## 30     11.9      12.4  Alcohol and drug abuse/misuse         Schäfer et al. 2019
## 31     11.5      12.5  All mental health outcomes            Schäfer et al. 2019
## 32     13.8      12.5  All mental health outcomes            Schäfer et al. 2019
## 33     11.7      12.5  All mental health outcomes            Schäfer et al. 2019
## 34     11.9      12.5  All mental health outcomes            Schäfer et al. 2019
## 35     11.2      10.7  All mental health outcomes            Schäfer et al. 2019
## 36     12        10.7  All mental health outcomes            Schäfer et al. 2019
## 37     11.5      11.8  All mental health outcomes            Schäfer et al. 2019
## 38     12.2      11.8  All mental health outcomes            Schäfer et al. 2019
## 39     11        11.4  All mental health outcomes            Schäfer et al. 2019
## 40     11.5      11.4  All mental health outcomes            Schäfer et al. 2019
## 41     11.9      10.3  All mental health outcomes            Schäfer et al. 2019
## 42     12.4      10.3  All mental health outcomes            Schäfer et al. 2019
##    effect_size sd_used            main_es_method    ppcor ppcor_method N_total
##    <chr>       <chr>              <chr>             <dbl> <chr>          <dbl>
##  1 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
##  2 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
##  3 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
##  4 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
##  5 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
##  6 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
##  7 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
##  8 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
##  9 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 10 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 11 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 12 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 13 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 14 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 15 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 16 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 17 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 18 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 19 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 20 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 21 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 22 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 23 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 24 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 25 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 26 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 27 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 28 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 29 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 30 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 31 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 32 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 33 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 34 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 35 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 36 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 37 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 38 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 39 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 40 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
## 41 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          232
## 42 SMD         Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          228
##    df_ind   m_post sd_pool   d_post vd_post Wd_post      J   g_post vg_post
##     <dbl>    <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>
##  1    232  0.03000  0.2703  0.1110  0.01727 0.01724 0.9968  0.1106  0.01727
##  2    228  0.08     0.2569  0.3114  0.01777 0.01756 0.9967  0.3104  0.01777
##  3    232  0.08     0.2521  0.3174  0.01746 0.01724 0.9968  0.3164  0.01746
##  4    228  0.03000  0.2705  0.1109  0.01758 0.01756 0.9967  0.1106  0.01758
##  5    232  0.05     0.2609  0.1916  0.01732 0.01724 0.9968  0.1910  0.01732
##  6    228  0.08     0.2613  0.3062  0.01776 0.01756 0.9967  0.3052  0.01776
##  7    232  0        0.1104  0       0.01724 0.01724 0.9968  0       0.01724
##  8    228  0        0.1050  0       0.01756 0.01756 0.9967  0       0.01756
##  9    232  0.03     0.1052  0.2853  0.01742 0.01724 0.9968  0.2843  0.01742
## 10    228  0.02     0.1053  0.1900  0.01764 0.01756 0.9967  0.1894  0.01763
## 11    232  0.02     0.11    0.1818  0.01731 0.01724 0.9968  0.1812  0.01731
## 12    228  0.0100   0.1150  0.08697 0.01757 0.01756 0.9967  0.08669 0.01757
## 13    232  0.2000  13.50    0.01481 0.01724 0.01724 0.9968  0.01476 0.01724
## 14    228  5.3     13.70    0.3869  0.01788 0.01756 0.9967  0.3856  0.01788
## 15    232  1.8     13.66    0.1318  0.01728 0.01724 0.9968  0.1314  0.01728
## 16    228  5.6     13.29    0.4213  0.01795 0.01756 0.9967  0.4199  0.01794
## 17    232 -0.4000  12.31   -0.03250 0.01724 0.01724 0.9968 -0.03240 0.01724
## 18    228  6.8     12.50    0.5439  0.01820 0.01756 0.9967  0.5421  0.01820
## 19    232  5.900   26.96    0.2189  0.01735 0.01724 0.9968  0.2182  0.01735
## 20    228 12.7     26.64    0.4767  0.01805 0.01756 0.9967  0.4751  0.01805
## 21    232  7.3     25.30    0.2885  0.01742 0.01724 0.9968  0.2876  0.01742
## 22    228 14.6     25.06    0.5826  0.01830 0.01756 0.9967  0.5806  0.01830
## 23    232  6.200   25.46    0.2435  0.01737 0.01724 0.9968  0.2427  0.01737
## 24    228 13.5     24.60    0.5488  0.01822 0.01756 0.9967  0.5470  0.01821
## 25    232 -3.5     11.09   -0.3156  0.01746 0.01724 0.9968 -0.3146  0.01746
## 26    228 -1.900   11.75   -0.1616  0.01761 0.01756 0.9967 -0.1611  0.01761
## 27    232 -6       11.75   -0.5106  0.01780 0.01724 0.9968 -0.5089  0.01780
## 28    228 -4.1     12.04   -0.3406  0.01781 0.01756 0.9967 -0.3394  0.01781
## 29    232 -3.100   12.06   -0.2571  0.01739 0.01724 0.9968 -0.2563  0.01738
## 30    228 -2       12.16   -0.1645  0.01762 0.01756 0.9967 -0.1639  0.01761
## 31    232  1.8     12.01    0.1498  0.01729 0.01724 0.9968  0.1493  0.01729
## 32    228  3.400   13.15    0.2586  0.01770 0.01756 0.9967  0.2577  0.01770
## 33    232  3.8     12.11    0.3138  0.01745 0.01724 0.9968  0.3128  0.01745
## 34    228  4.3     12.21    0.3521  0.01783 0.01756 0.9967  0.3510  0.01783
## 35    232  1       10.95    0.09132 0.01726 0.01724 0.9968  0.09102 0.01726
## 36    228  3.2     11.35    0.2819  0.01773 0.01756 0.9967  0.2810  0.01773
## 37    232  0.8000  11.65    0.06866 0.01725 0.01724 0.9968  0.06843 0.01725
## 38    228  2.400   12.00    0.2001  0.01764 0.01756 0.9967  0.1994  0.01764
## 39    232  3.6     11.20    0.3213  0.01747 0.01724 0.9968  0.3203  0.01746
## 40    228  2.2     11.45    0.1922  0.01764 0.01756 0.9967  0.1915  0.01764
## 41    232  1.8     11.12    0.1618  0.01730 0.01724 0.9968  0.1613  0.01730
## 42    228  3.200   11.37    0.2814  0.01773 0.01756 0.9967  0.2805  0.01773
##    Wg_post m_diff_t m_diff_c     d_DD   vd_DD   Wd_DD     g_DD   vg_DD   Wg_DD
##      <dbl>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
##  1 0.01724  0.08     0.0400   0.1480  0.01729 0.01724  0.1475  0.01729 0.01724
##  2 0.01756  0.08     0.0400   0.1557  0.01761 0.01756  0.1552  0.01761 0.01756
##  3 0.01724  0.14     0.05     0.3571  0.01752 0.01724  0.3559  0.01752 0.01724
##  4 0.01756  0.04000  0.05    -0.03698 0.01756 0.01756 -0.03685 0.01756 0.01756
##  5 0.01724  0.08     0.02000  0.2299  0.01736 0.01724  0.2292  0.01736 0.01724
##  6 0.01756  0.06     0.02000  0.1531  0.01761 0.01756  0.1526  0.01761 0.01756
##  7 0.01724  0.04     0.03     0.09061 0.01726 0.01724  0.09031 0.01726 0.01724
##  8 0.01756  0.03     0.03     0       0.01756 0.01756  0       0.01756 0.01756
##  9 0.01724  0.06     0.0200   0.3804  0.01755 0.01724  0.3791  0.01755 0.01724
## 10 0.01756  0.04     0.0200   0.1900  0.01764 0.01756  0.1894  0.01763 0.01756
## 11 0.01724  0.04     0.0100   0.2727  0.01740 0.01724  0.2718  0.01740 0.01724
## 12 0.01756  0.0200   0.0100   0.08697 0.01757 0.01756  0.08669 0.01757 0.01756
## 13 0.01724  4.4      3.400    0.07405 0.01725 0.01724  0.07381 0.01725 0.01724
## 14 0.01756  5.4      3.400    0.1460  0.01760 0.01756  0.1455  0.01760 0.01756
## 15 0.01724  7.1      4.5      0.1904  0.01732 0.01724  0.1898  0.01732 0.01724
## 16 0.01756  6.8      4.5      0.1730  0.01762 0.01756  0.1725  0.01762 0.01756
## 17 0.01724  3.2      2.8      0.03250 0.01724 0.01724  0.03240 0.01724 0.01724
## 18 0.01756  6.3      2.8      0.2799  0.01773 0.01756  0.2790  0.01773 0.01756
## 19 0.01724  9.8      5.100    0.1744  0.01731 0.01724  0.1738  0.01731 0.01724
## 20 0.01756  6.400    5.100    0.04880 0.01756 0.01756  0.04864 0.01756 0.01756
## 21 0.01724 10.7      4.600    0.2411  0.01737 0.01724  0.2403  0.01737 0.01724
## 22 0.01756  7.8      4.600    0.1277  0.01759 0.01756  0.1273  0.01759 0.01756
## 23 0.01724  7.8      2.800    0.1964  0.01733 0.01724  0.1958  0.01733 0.01724
## 24 0.01756  4.900    2.800    0.08537 0.01757 0.01756  0.08509 0.01757 0.01756
## 25 0.01724 -5.5     -2.000   -0.3156  0.01746 0.01724 -0.3146  0.01746 0.01724
## 26 0.01756 -2.9     -2.000   -0.07656 0.01757 0.01756 -0.07631 0.01757 0.01756
## 27 0.01724 -6.8     -0.8000  -0.5106  0.01780 0.01724 -0.5089  0.01780 0.01724
## 28 0.01756 -3.9     -0.8000  -0.2575  0.01770 0.01756 -0.2566  0.01770 0.01756
## 29 0.01724 -3.8     -0.7000  -0.2571  0.01739 0.01724 -0.2563  0.01738 0.01724
## 30 0.01756 -1.7     -0.7000  -0.08224 0.01757 0.01756 -0.08197 0.01757 0.01756
## 31 0.01724  5.1      3.4      0.1415  0.01729 0.01724  0.1410  0.01729 0.01724
## 32 0.01756  4.8      3.4      0.1065  0.01758 0.01756  0.1061  0.01758 0.01756
## 33 0.01724  7.7      4        0.3055  0.01744 0.01724  0.3045  0.01744 0.01724
## 34 0.01756  6.3      4        0.1883  0.01763 0.01756  0.1877  0.01763 0.01756
## 35 0.01724  4.6      3.7      0.08219 0.01726 0.01724  0.08192 0.01726 0.01724
## 36 0.01756  4.9      3.7      0.1057  0.01758 0.01756  0.1054  0.01758 0.01756
## 37 0.01724  3.8      4.4     -0.05149 0.01725 0.01724 -0.05133 0.01725 0.01724
## 38 0.01756  3.300    4.4     -0.09169 0.01757 0.01756 -0.09139 0.01757 0.01756
## 39 0.01724  6.8      4.6      0.1964  0.01733 0.01724  0.1957  0.01733 0.01724
## 40 0.01756  3.300    4.6     -0.1135  0.01758 0.01756 -0.1132  0.01758 0.01756
## 41 0.01724  3.2      2.800    0.03597 0.01725 0.01724  0.03585 0.01725 0.01724
## 42 0.01756  2.5      2.800   -0.02638 0.01756 0.01756 -0.02630 0.01756 0.01756
##    avg_cl_size avg_cl_type         icc icc_type n_covariates gamma_sqrt df_adj
##          <dbl> <chr>             <dbl> <chr>           <dbl>      <dbl>  <dbl>
##  1           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
##  2           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
##  3           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
##  4           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
##  5           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
##  6           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
##  7           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
##  8           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
##  9           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 10           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 11           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 12           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 13           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 14           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 15           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 16           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 17           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 18           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 19           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 20           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 21           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 22           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 23           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 24           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 25           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 26           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 27           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 28           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 29           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 30           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 31           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 32           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 33           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 34           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 35           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 36           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 37           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 38           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 39           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 40           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
## 41           8 From study (p. 4)   0.1 Imputed             1      0.973  221.3
## 42           8 From study (p. 4)   0.1 Imputed             1      0.972  217.6
##     omega  gt_post vgt_post Wgt_post    gt_DD  vgt_DD  Wgt_DD    hg_DD   vhg_DD
##     <dbl>    <dbl>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
##  1 0.9966  0.1076   0.02249  0.02247  0.1435  0.02251 0.02247  0.06433 0.004518
##  2 0.9965  0.3017   0.02323  0.02302  0.1508  0.02307 0.02302  0.06737 0.004595
##  3 0.9966  0.3078   0.02268  0.02247  0.3462  0.02274 0.02247  0.1550  0.004518
##  4 0.9965  0.1074   0.02304  0.02302 -0.03582 0.02302 0.02302 -0.01600 0.004595
##  5 0.9966  0.1858   0.02255  0.02247  0.2230  0.02258 0.02247  0.09990 0.004518
##  6 0.9965  0.2966   0.02322  0.02302  0.1483  0.02307 0.02302  0.06623 0.004595
##  7 0.9966  0        0.02247  0.02247  0.08786 0.02248 0.02247  0.03939 0.004518
##  8 0.9965  0        0.02302  0.02302  0       0.02302 0.02302  0       0.004595
##  9 0.9966  0.2766   0.02264  0.02247  0.3688  0.02277 0.02247  0.1650  0.004518
## 10 0.9965  0.1841   0.02309  0.02302  0.1841  0.02309 0.02302  0.08220 0.004595
## 11 0.9966  0.1763   0.02254  0.02247  0.2645  0.02263 0.02247  0.1185  0.004518
## 12 0.9965  0.08425  0.02303  0.02302  0.08425 0.02303 0.02302  0.03764 0.004595
## 13 0.9966  0.01436  0.02247  0.02247  0.07180 0.02248 0.02247  0.03220 0.004518
## 14 0.9965  0.3748   0.02334  0.02302  0.1414  0.02306 0.02302  0.06317 0.004595
## 15 0.9966  0.1278   0.02250  0.02247  0.1846  0.02254 0.02247  0.08273 0.004518
## 16 0.9965  0.4081   0.02340  0.02302  0.1676  0.02308 0.02302  0.07486 0.004595
## 17 0.9966 -0.03152  0.02247  0.02247  0.03152 0.02247 0.02247  0.01413 0.004518
## 18 0.9965  0.5268   0.02365  0.02302  0.2712  0.02318 0.02302  0.1210  0.004595
## 19 0.9966  0.2122   0.02257  0.02247  0.1691  0.02253 0.02247  0.07578 0.004518
## 20 0.9965  0.4618   0.02351  0.02302  0.04727 0.02302 0.02302  0.02112 0.004595
## 21 0.9966  0.2798   0.02264  0.02247  0.2338  0.02259 0.02247  0.1047  0.004518
## 22 0.9965  0.5643   0.02375  0.02302  0.1237  0.02305 0.02302  0.05525 0.004595
## 23 0.9966  0.2361   0.02259  0.02247  0.1904  0.02255 0.02247  0.08535 0.004518
## 24 0.9965  0.5316   0.02367  0.02302  0.08270 0.02303 0.02302  0.03695 0.004595
## 25 0.9966 -0.3060   0.02268  0.02247 -0.3060  0.02268 0.02247 -0.1370  0.004518
## 26 0.9965 -0.1566   0.02307  0.02302 -0.07416 0.02303 0.02302 -0.03313 0.004595
## 27 0.9966 -0.4951   0.02302  0.02247 -0.4951  0.02302 0.02247 -0.2211  0.004518
## 28 0.9965 -0.3299   0.02327  0.02302 -0.2494  0.02316 0.02302 -0.1113  0.004595
## 29 0.9966 -0.2493   0.02261  0.02247 -0.2493  0.02261 0.02247 -0.1117  0.004518
## 30 0.9965 -0.1593   0.02307  0.02302 -0.07966 0.02303 0.02302 -0.03559 0.004595
## 31 0.9966  0.1453   0.02251  0.02247  0.1372  0.02251 0.02247  0.06151 0.004518
## 32 0.9965  0.2505   0.02316  0.02302  0.1031  0.02304 0.02302  0.04607 0.004595
## 33 0.9966  0.3043   0.02268  0.02247  0.2963  0.02267 0.02247  0.1327  0.004518
## 34 0.9965  0.3411   0.02328  0.02302  0.1824  0.02309 0.02302  0.08147 0.004595
## 35 0.9966  0.08855  0.02248  0.02247  0.07970 0.02248 0.02247  0.03573 0.004518
## 36 0.9965  0.2731   0.02319  0.02302  0.1024  0.02304 0.02302  0.04575 0.004595
## 37 0.9966  0.06658  0.02248  0.02247 -0.04993 0.02247 0.02247 -0.02239 0.004518
## 38 0.9965  0.1938   0.02310  0.02302 -0.08882 0.02303 0.02302 -0.03968 0.004595
## 39 0.9966  0.3116   0.02269  0.02247  0.1904  0.02255 0.02247  0.08534 0.004518
## 40 0.9965  0.1861   0.02310  0.02302 -0.1100  0.02304 0.02302 -0.04913 0.004595
## 41 0.9966  0.1569   0.02252  0.02247  0.03488 0.02247 0.02247  0.01564 0.004518
## 42 0.9965  0.2726   0.02319  0.02302 -0.02556 0.02302 0.02302 -0.01142 0.004595
##     h_DD df_DD n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop
##    <dbl> <dbl>           <dbl> <chr>             <dbl>     <dbl>      <dbl>
##  1 221.3 221.3               1 eta               1.303  NA         NA      
##  2 217.6 217.6               1 eta               1.311  NA         NA      
##  3 221.3 221.3               1 eta               1.303  NA         NA      
##  4 217.6 217.6               1 eta               1.311  NA         NA      
##  5 221.3 221.3               1 eta               1.303  NA         NA      
##  6 217.6 217.6               1 eta               1.311  NA         NA      
##  7 221.3 221.3               1 eta               1.303  NA         NA      
##  8 217.6 217.6               1 eta               1.311  NA         NA      
##  9 221.3 221.3               1 eta               1.303  NA         NA      
## 10 217.6 217.6               1 eta               1.311  NA         NA      
## 11 221.3 221.3               1 eta               1.303  NA         NA      
## 12 217.6 217.6               1 eta               1.311  NA         NA      
## 13 221.3 221.3               1 eta               1.303   0.08095    0.02184
## 14 217.6 217.6               1 eta               1.311   0.1617     0.02241
## 15 221.3 221.3               1 eta               1.303   0.2105     0.02190
## 16 217.6 217.6               1 eta               1.311   0.1860     0.02242
## 17 221.3 221.3               1 eta               1.303   0.03238    0.02183
## 18 217.6 217.6               1 eta               1.311   0.2830     0.02250
## 19 221.3 221.3               1 eta               1.303  NA         NA      
## 20 217.6 217.6               1 eta               1.311  NA         NA      
## 21 221.3 221.3               1 eta               1.303  NA         NA      
## 22 217.6 217.6               1 eta               1.311  NA         NA      
## 23 221.3 221.3               1 eta               1.303  NA         NA      
## 24 217.6 217.6               1 eta               1.311  NA         NA      
## 25 221.3 221.3               1 eta               1.303  NA         NA      
## 26 217.6 217.6               1 eta               1.311  NA         NA      
## 27 221.3 221.3               1 eta               1.303  NA         NA      
## 28 217.6 217.6               1 eta               1.311  NA         NA      
## 29 221.3 221.3               1 eta               1.303  NA         NA      
## 30 217.6 217.6               1 eta               1.311  NA         NA      
## 31 221.3 221.3               1 eta               1.303  NA         NA      
## 32 217.6 217.6               1 eta               1.311  NA         NA      
## 33 221.3 221.3               1 eta               1.303  NA         NA      
## 34 217.6 217.6               1 eta               1.311  NA         NA      
## 35 221.3 221.3               1 eta               1.303  NA         NA      
## 36 217.6 217.6               1 eta               1.311  NA         NA      
## 37 221.3 221.3               1 eta               1.303  NA         NA      
## 38 217.6 217.6               1 eta               1.311  NA         NA      
## 39 221.3 221.3               1 eta               1.303  NA         NA      
## 40 217.6 217.6               1 eta               1.311  NA         NA      
## 41 221.3 221.3               1 eta               1.303  NA         NA      
## 42 217.6 217.6               1 eta               1.311  NA         NA      
##    Wgt_DD_pop vary_id                                                    
##         <dbl> <chr>                                                      
##  1   NA       ASI alcohol severity/3m/Relapse Prevention Training        
##  2   NA       ASI alcohol severity/3m/Seeking Safety                     
##  3   NA       ASI alcohol severity/6m/Relapse Prevention Training        
##  4   NA       ASI alcohol severity/6m/Seeking Safety                     
##  5   NA       ASI alcohol severity/Post/Relapse Prevention Training      
##  6   NA       ASI alcohol severity/Post/Seeking Safety                   
##  7   NA       ASI drug severity/3m/Relapse Prevention Training           
##  8   NA       ASI drug severity/3m/Seeking Safety                        
##  9   NA       ASI drug severity/6m/Relapse Prevention Training           
## 10   NA       ASI drug severity/6m/Seeking Safety                        
## 11   NA       ASI drug severity/Post/Relapse Prevention Training         
## 12   NA       ASI drug severity/Post/Seeking Safety                      
## 13    0.02183 BDI-II/3m/Relapse Prevention Training                      
## 14    0.02236 BDI-II/3m/Seeking Safety                                   
## 15    0.02183 BDI-II/6m/Relapse Prevention Training                      
## 16    0.02236 BDI-II/6m/Seeking Safety                                   
## 17    0.02183 BDI-II/Post/Relapse Prevention Training                    
## 18    0.02236 BDI-II/Post/Seeking Safety                                 
## 19   NA       DERS/3m/Relapse Prevention Training                        
## 20   NA       DERS/3m/Seeking Safety                                     
## 21   NA       DERS/6m/Relapse Prevention Training                        
## 22   NA       DERS/6m/Seeking Safety                                     
## 23   NA       DERS/Post/Relapse Prevention Training                      
## 24   NA       DERS/Post/Seeking Safety                                   
## 25   NA       Drug and alcohol free days/3m/Relapse Prevention Training  
## 26   NA       Drug and alcohol free days/3m/Seeking Safety               
## 27   NA       Drug and alcohol free days/6m/Relapse Prevention Training  
## 28   NA       Drug and alcohol free days/6m/Seeking Safety               
## 29   NA       Drug and alcohol free days/Post/Relapse Prevention Training
## 30   NA       Drug and alcohol free days/Post/Seeking Safety             
## 31   NA       PDS/3m/Relapse Prevention Training                         
## 32   NA       PDS/3m/Seeking Safety                                      
## 33   NA       PDS/6m/Relapse Prevention Training                         
## 34   NA       PDS/6m/Seeking Safety                                      
## 35   NA       PDS/Post/Relapse Prevention Training                       
## 36   NA       PDS/Post/Seeking Safety                                    
## 37   NA       PSS-I/3m/Relapse Prevention Training                       
## 38   NA       PSS-I/3m/Seeking Safety                                    
## 39   NA       PSS-I/6m/Relapse Prevention Training                       
## 40   NA       PSS-I/6m/Seeking Safety                                    
## 41   NA       PSS-I/Post/Relapse Prevention Training                     
## 42   NA       PSS-I/Post/Seeking Safety

Somers et al. (2017) (Quality-checked)

Data extracted from Table 1 (p. 7) and Table 3 (p. 9)

Somers2017_est <- 
  tibble(
    
    study = "Somers et al. 2017",
    outcome = c(
      "Physical CIS",
      "Psychological CIS",
      "CSI",
      "GAIN-SPS",
      "QOL20"
    ),
    
    analysis_plan = c(
      "Social functioning (degree of impairment)",
      "Social functioning (degree of impairment)",
      "All mental health outcomes",
      "Alcohol and drug abuse/misuse",
      "Wellbeing and Quality of Life"
    ),
    analysis = "ITT",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    
    N_start_t = 90,
    N_start_c = 100,
    
    N_t = N_start_t,
    N_c = N_start_c,
    
    N_total = N_t + N_c, 
    df_ind = N_total,
    
    m_pre_t = c(2.1, 10.61, 37.12, 2.39, 72.61), 
    m_pre_c = c(1.83, 11.1, 40.25, 2.29, 74.72),
    
    sd_pre_t = c(1.75, 3.68, 12.91, 1.94, 21.69),
    sd_pre_c = c(1.70, 3.19, 12.49, 1.92, 21.43),
    
    m_post_t = c(2.82, 14.66, 26.25, 1.34, 91.80), 
    m_post_c = c(2.07, 12.62, 27.7, 1, 87.80),
    
    sd_post_t = c(1.90, 3.70, 10.98, 1.67, 24.55),
    sd_post_c = c(1.79, 3.70, 11.80, 1.57, 22.71),
    
    m_diff_t = c(0.72, 4.05, -10.87, -1.05, 19.19),
    cil_m_diff_t = c(0.32, 3.15, -13.42, -1.51, 14.34),
    ciu_m_diff_t = c(1.11, 4.94, -8.32, -0.59, 24.05),
    
    sd_diff_t = sqrt(N_t) * (ciu_m_diff_t - cil_m_diff_t)/3.92,
    
    m_diff_c = c(0.24, 1.52, -12.55, -1.29, 13.09),
    cil_m_diff_c = c(-0.15, 0.63, -15.31, -1.71, 8.01),
    ciu_m_diff_c = c(0.64, 2.40, -9.78, -0.88, 18.16),
    
    sd_diff_c = sqrt(N_c) * (ciu_m_diff_c - cil_m_diff_c)/3.92,
    
    
  ) |> 
  mutate(
    
    r_t = (sd_pre_t^2 + sd_post_t^2 - sd_diff_t^2)/(2*sd_pre_t*sd_post_t),
    
    z_t = 0.5 * log( (1+r_t)/(1-r_t) ),
    v_t = 1/(N_t-3),
    w_t = 1/v_t,
    
    r_c = (sd_pre_c^2 + sd_post_c^2 - sd_diff_c^2)/(2*sd_pre_c*sd_post_c),
    
    z_c = 0.5 * log( (1+r_c)/(1-r_c) ),
    v_c = 1/(N_c-3),
    w_c = 1/v_c,
    
    mean_z =  (w_t*z_t + w_c*z_c)/(w_t + w_c),  
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "Calculated from study results",
   
    m_post = if_else(str_detect(outcome, "CIS|QOL"), (m_post_t - m_post_c), (m_post_t - m_post_c)*-1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For YMRS and HAM-D lower scores are beneficial why these are reverted
    m_diff_t = if_else(str_detect(outcome, "CIS|QOL"), (m_post_t - m_pre_t), (m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(str_detect(outcome, "CIS|QOL"), (m_post_c - m_pre_c), (m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate( 
    
    # Could not find any estimate
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ), 
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Somers2017_est
## # A tibble: 5 × 77
##   study              outcome           analysis_plan                            
##   <chr>              <chr>             <chr>                                    
## 1 Somers et al. 2017 Physical CIS      Social functioning (degree of impairment)
## 2 Somers et al. 2017 Psychological CIS Social functioning (degree of impairment)
## 3 Somers et al. 2017 CSI               All mental health outcomes               
## 4 Somers et al. 2017 GAIN-SPS          Alcohol and drug abuse/misuse            
## 5 Somers et al. 2017 QOL20             Wellbeing and Quality of Life            
##   analysis effect_size sd_used             main_es_method    N_start_t N_start_c
##   <chr>    <chr>       <chr>               <chr>                 <dbl>     <dbl>
## 1 ITT      SMD         Pooled posttest SDs Raw diff-in-diffs        90       100
## 2 ITT      SMD         Pooled posttest SDs Raw diff-in-diffs        90       100
## 3 ITT      SMD         Pooled posttest SDs Raw diff-in-diffs        90       100
## 4 ITT      SMD         Pooled posttest SDs Raw diff-in-diffs        90       100
## 5 ITT      SMD         Pooled posttest SDs Raw diff-in-diffs        90       100
##     N_t   N_c N_total df_ind m_pre_t m_pre_c sd_pre_t sd_pre_c m_post_t m_post_c
##   <dbl> <dbl>   <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
## 1    90   100     190    190    2.1     1.83     1.75     1.7      2.82     2.07
## 2    90   100     190    190   10.61   11.1      3.68     3.19    14.66    12.62
## 3    90   100     190    190   37.12   40.25    12.91    12.49    26.25    27.7 
## 4    90   100     190    190    2.39    2.29     1.94     1.92     1.34     1   
## 5    90   100     190    190   72.61   74.72    21.69    21.43    91.8     87.8 
##   sd_post_t sd_post_c m_diff_t cil_m_diff_t ciu_m_diff_t sd_diff_t m_diff_c
##       <dbl>     <dbl>    <dbl>        <dbl>        <dbl>     <dbl>    <dbl>
## 1      1.9       1.79     0.72         0.32         1.11     1.912   0.2400
## 2      3.7       3.7      4.05         3.15         4.94     4.332   1.52  
## 3     10.98     11.8     10.87       -13.42        -8.32    12.34   12.55  
## 4      1.67      1.57     1.05        -1.51        -0.59     2.227   1.29  
## 5     24.55     22.71    19.19        14.34        24.05    23.50   13.08  
##   cil_m_diff_c ciu_m_diff_c sd_diff_c    r_t    z_t     v_t   w_t    r_c    z_c
##          <dbl>        <dbl>     <dbl>  <dbl>  <dbl>   <dbl> <dbl>  <dbl>  <dbl>
## 1        -0.15         0.64     2.015 0.4537 0.4894 0.01149    87 0.3340 0.3473
## 2         0.63         2.4      4.515 0.3109 0.3215 0.01149    87 0.1473 0.1484
## 3       -15.31        -9.78    14.11  0.4758 0.5175 0.01149    87 0.3265 0.3389
## 4        -1.71        -0.88     2.117 0.2462 0.2513 0.01149    87 0.2767 0.2841
## 5         8.01        18.16    25.89  0.4892 0.5350 0.01149    87 0.3129 0.3237
##       v_c   w_c mean_z  ppcor ppcor_method                  m_post sd_pool
##     <dbl> <dbl>  <dbl>  <dbl> <chr>                          <dbl>   <dbl>
## 1 0.01031    97 0.4145 0.3923 Calculated from study results   0.75   1.843
## 2 0.01031    97 0.2303 0.2263 Calculated from study results   2.04   3.7  
## 3 0.01031    97 0.4233 0.3997 Calculated from study results   1.45  11.42 
## 4 0.01031    97 0.2686 0.2623 Calculated from study results  -0.34   1.618
## 5 0.01031    97 0.4236 0.4000 Calculated from study results   4     23.60 
##    d_post vd_post Wd_post      J  g_post vg_post Wg_post    d_DD   vd_DD   Wd_DD
##     <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1  0.4070 0.02155 0.02111 0.9960  0.4054 0.02154 0.02111  0.2605 0.02584 0.02566
## 2  0.5514 0.02191 0.02111 0.9960  0.5492 0.02190 0.02111  0.6838 0.03390 0.03267
## 3  0.1270 0.02115 0.02111 0.9960  0.1265 0.02115 0.02111 -0.1471 0.02540 0.02534
## 4 -0.2101 0.02123 0.02111 0.9960 -0.2093 0.02123 0.02111 -0.1483 0.03120 0.03115
## 5  0.1695 0.02119 0.02111 0.9960  0.1688 0.02119 0.02111  0.2589 0.02551 0.02533
##      g_DD   vg_DD   Wg_DD avg_cl_size avg_cl_type   icc icc_type n_covariates
##     <dbl>   <dbl>   <dbl>       <dbl> <chr>       <dbl> <chr>           <dbl>
## 1  0.2594 0.02584 0.02566           8 Imputed       0.1 Imputed             1
## 2  0.6811 0.03389 0.03267           8 Imputed       0.1 Imputed             1
## 3 -0.1465 0.02540 0.02534           8 Imputed       0.1 Imputed             1
## 4 -0.1477 0.03120 0.03115           8 Imputed       0.1 Imputed             1
## 5  0.2579 0.02551 0.02533           8 Imputed       0.1 Imputed             1
##   gamma_sqrt df_adj  omega gt_post vgt_post Wgt_post   gt_DD  vgt_DD  Wgt_DD
##        <dbl>  <dbl>  <dbl>   <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>
## 1      0.971  181.3 0.9959  0.3935  0.02831  0.02789  0.2519 0.03407 0.03390
## 2      0.971  181.3 0.9959  0.5331  0.02867  0.02789  0.6612 0.04436 0.04315
## 3      0.971  181.3 0.9959  0.1228  0.02793  0.02789 -0.1423 0.03354 0.03348
## 4      0.971  181.3 0.9959 -0.2032  0.02800  0.02789 -0.1434 0.04120 0.04114
## 5      0.971  181.3 0.9959  0.1639  0.02796  0.02789  0.2504 0.03364 0.03347
##      hg_DD   vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD adj_value_DD
##      <dbl>    <dbl> <dbl> <dbl>           <dbl> <chr>             <dbl>
## 1  0.1015  0.005516 181.3 181.3               1 eta               1.321
## 2  0.2353  0.005516 181.3 181.3               1 eta               1.321
## 3 -0.05773 0.005516 181.3 181.3               1 eta               1.321
## 4 -0.05250 0.005516 181.3 181.3               1 eta               1.321
## 5  0.1016  0.005516 181.3 181.3               1 eta               1.321
##   vary_id          
##   <chr>            
## 1 Physical CIS     
## 2 Psychological CIS
## 3 CSI              
## 4 GAIN-SPS         
## 5 QOL20

Tjaden et al. (2021) (Quality-checked)

Entering data from Table 2 (p. 1313)

Tjaden2021 <- tibble(
  group = rep(c("Ressource group + FACT", #Ressource group
                "FACT"),                  #Control group
  each = 1, 8),
  

  outcome = rep(c("NEL",     # Netherlands Empowerment List
                  "BSI",     # Brief Symptom Inventory
                  "MANSA",   # Manchester Short assesment of Quality of Life
                  "GAF"),    # Global Assesment of functioning 
  each = 2, 2), 
 
  
  timing = rep(c("9m", "18m"), each = 8), 
  
   N_start = rep(c(
    80, 78,
    80, 78,
    80, 78,
    80, 78), 
  each = 1, 2),
  
  N = c(
    # 9 months
    70, 67,
    70, 67,
    70, 67,
    70, 67,
    
    # 18 months
    63, 58,
    63, 58,
    63, 58,
    63, 58),
  
   
  m_pre = rep(c(
    3.32, 3.34,
    2.11, 2.15,
    4.12, 4.26,
    47.91, 51.45),
    each = 1,2), 
  
  
  sd_pre = rep(c(
    0.51, 0.52,
    0.72, 0.85,
    0.88, 0.85,
    10.22, 10.58), 
    each = 1,2), 
  
  m_post = c(
    # 9 months
    3.55, 3.34,
    1.98, 1.98,
    4.49, 4.34,
    53.8, 54.03,
    
    # 18 months
    3.77, 3.38,
    1.92, 1.91,
    4.67, 4.48,
    58.33, 54.84),
  
  sd_post = c(
    # 9 months
    0.53, 0.62,
    0.86, 0.81,
    0.74, 0.90,
    10.16, 11.26,
    
    # 18 months
    0.57, 0.70,
    0.82, 0.76,
    0.74, 1.04,
    11.76, 13.43)
  
)

# Turning data into wide format


Tjaden2021_wide <- Tjaden2021 |> 
      mutate(group = case_match(group, "Ressource group + FACT" ~ "t", "FACT" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N_start:last_col()
      ) #|> 
 # arrange(outcome, desc(timing))

Tjaden2021_est <- Tjaden2021_wide |> 
  # Based on the degrees of freedom value reported in Druss et al. 2018 table 2 and 3
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "BSI") ~ "All mental health outcomes",
      str_detect(outcome, "GAF") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "MANSA") ~ "Wellbeing and Quality of Life",
      str_detect(outcome, "NEL") ~ "Hope, Empowerment & Self-efficacy",
      .default = NA_character_
    ),
    
  # Effect sizes are acorss different time    
  #  d = c(
  #    rep(c(NA_real_), each = 4,1),
  #    c(0.54,
  #       0.25,
  #      0.07,
  #       0.30)),
    
  study = "Tjaden et al. 2021",
  
  effect_size = "SMD",
  sd_used = "Pooled posttest SD",
  main_es_method = "Raw diff-in-diffs",
  
  
  # ppcor imputed
  ppcor = ppcor_imp, 
  ppcor_method = "Imputed"
  
  )|> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    # For PANSS: reverted
    m_post = if_else(outcome %in% c("BSI"), 
                     (m_post_t - m_post_c)*-1,
                     m_post_t - m_post_c),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # For all scores: Lower is beneficial
    m_diff_t = if_else(outcome %in% c("BSI"), (m_post_t - m_pre_t)*-1, m_post_t - m_post_c),
    m_diff_c = if_else(outcome %in% c("BSI"), (m_post_c - m_pre_c)*-1,m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # Group size is imputed
    
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * ((m_diff_t - m_diff_c)/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
   vary_id = paste0(outcome, "/", timing)
   ) |> 
   ungroup(); Tjaden2021_est
## # A tibble: 8 × 65
##   outcome timing N_start_t N_start_c   N_t   N_c m_pre_t m_pre_c sd_pre_t
##   <chr>   <chr>      <dbl>     <dbl> <dbl> <dbl>   <dbl>   <dbl>    <dbl>
## 1 NEL     9m            80        78    70    67    3.32    3.34     0.51
## 2 BSI     9m            80        78    70    67    2.11    2.15     0.72
## 3 MANSA   9m            80        78    70    67    4.12    4.26     0.88
## 4 GAF     9m            80        78    70    67   47.91   51.45    10.22
## 5 NEL     18m           80        78    63    58    3.32    3.34     0.51
## 6 BSI     18m           80        78    63    58    2.11    2.15     0.72
## 7 MANSA   18m           80        78    63    58    4.12    4.26     0.88
## 8 GAF     18m           80        78    63    58   47.91   51.45    10.22
##   sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##      <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
## 1     0.52     3.55     3.34      0.53      0.62
## 2     0.85     1.98     1.98      0.86      0.81
## 3     0.85     4.49     4.34      0.74      0.9 
## 4    10.58    53.8     54.03     10.16     11.26
## 5     0.52     3.77     3.38      0.57      0.7 
## 6     0.85     1.92     1.91      0.82      0.76
## 7     0.85     4.67     4.48      0.74      1.04
## 8    10.58    58.33    54.84     11.76     13.43
##   analysis_plan                             study              effect_size
##   <chr>                                     <chr>              <chr>      
## 1 Hope, Empowerment & Self-efficacy         Tjaden et al. 2021 SMD        
## 2 All mental health outcomes                Tjaden et al. 2021 SMD        
## 3 Wellbeing and Quality of Life             Tjaden et al. 2021 SMD        
## 4 Social functioning (degree of impairment) Tjaden et al. 2021 SMD        
## 5 Hope, Empowerment & Self-efficacy         Tjaden et al. 2021 SMD        
## 6 All mental health outcomes                Tjaden et al. 2021 SMD        
## 7 Wellbeing and Quality of Life             Tjaden et al. 2021 SMD        
## 8 Social functioning (degree of impairment) Tjaden et al. 2021 SMD        
##   sd_used            main_es_method    ppcor ppcor_method N_total df_ind
##   <chr>              <chr>             <dbl> <chr>          <dbl>  <dbl>
## 1 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          137    137
## 2 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          137    137
## 3 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          137    137
## 4 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          137    137
## 5 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          121    121
## 6 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          121    121
## 7 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          121    121
## 8 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          121    121
##     m_post sd_pool   d_post vd_post Wd_post      J   g_post vg_post Wg_post
##      <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>   <dbl>
## 1  0.21     0.5758  0.3647  0.02970 0.02921 0.9945  0.3627  0.02969 0.02921
## 2  0        0.8359  0       0.02921 0.02921 0.9945  0       0.02921 0.02921
## 3  0.1500   0.8221  0.1825  0.02933 0.02921 0.9945  0.1815  0.02933 0.02921
## 4 -0.2300  10.71   -0.02147 0.02921 0.02921 0.9945 -0.02135 0.02921 0.02921
## 5  0.39     0.6356  0.6136  0.03467 0.03311 0.9938  0.6098  0.03465 0.03311
## 6 -0.01000  0.7918 -0.01263 0.03312 0.03311 0.9938 -0.01255 0.03312 0.03311
## 7  0.1900   0.8963  0.2120  0.03330 0.03311 0.9938  0.2107  0.03330 0.03311
## 8  3.490   12.59    0.2773  0.03343 0.03311 0.9938  0.2755  0.03343 0.03311
##   m_diff_t m_diff_c      d_DD   vd_DD   Wd_DD      g_DD   vg_DD   Wg_DD
##      <dbl>    <dbl>     <dbl>   <dbl>   <dbl>     <dbl>   <dbl>   <dbl>
## 1   0.21    0        0.3647   0.02970 0.02921  0.3627   0.02969 0.02921
## 2   0.1300  0.17    -0.04785  0.02922 0.02921 -0.04759  0.02922 0.02921
## 3   0.1500  0.08000  0.08515  0.02924 0.02921  0.08468  0.02924 0.02921
## 4  -0.2300  2.580   -0.2623   0.02946 0.02921 -0.2609   0.02946 0.02921
## 5   0.39    0.04000  0.5507   0.03437 0.03311  0.5472   0.03435 0.03311
## 6   0.19    0.24    -0.06315  0.03313 0.03311 -0.06275  0.03313 0.03311
## 7   0.1900  0.2200  -0.03347  0.03312 0.03311 -0.03326  0.03312 0.03311
## 8   3.490   3.39     0.007944 0.03311 0.03311  0.007895 0.03311 0.03311
##   avg_cl_size avg_cl_type   icc icc_type n_covariates gamma_sqrt df_adj  omega
##         <dbl> <chr>       <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
## 1           8 Imputed       0.1 Imputed             1      0.973  130.0 0.9942
## 2           8 Imputed       0.1 Imputed             1      0.973  130.0 0.9942
## 3           8 Imputed       0.1 Imputed             1      0.973  130.0 0.9942
## 4           8 Imputed       0.1 Imputed             1      0.973  130.0 0.9942
## 5           8 Imputed       0.1 Imputed             1      0.973  114.5 0.9934
## 6           8 Imputed       0.1 Imputed             1      0.973  114.5 0.9934
## 7           8 Imputed       0.1 Imputed             1      0.973  114.5 0.9934
## 8           8 Imputed       0.1 Imputed             1      0.973  114.5 0.9934
##    gt_post vgt_post Wgt_post     gt_DD  vgt_DD  Wgt_DD     hg_DD   vhg_DD  h_DD
##      <dbl>    <dbl>    <dbl>     <dbl>   <dbl>   <dbl>     <dbl>    <dbl> <dbl>
## 1  0.3528   0.03819  0.03771  0.3528   0.03819 0.03771  0.1590   0.007694 130.0
## 2  0        0.03771  0.03771 -0.04629  0.03772 0.03771 -0.02091  0.007694 130.0
## 3  0.1765   0.03783  0.03771  0.08237  0.03774 0.03771  0.03720  0.007694 130.0
## 4 -0.02077  0.03771  0.03771 -0.2538   0.03796 0.03771 -0.1145   0.007694 130.0
## 5  0.5931   0.04402  0.04249  0.5323   0.04372 0.04249  0.2401   0.008731 114.5
## 6 -0.01221  0.04249  0.04249 -0.06104  0.04250 0.04249 -0.02767  0.008731 114.5
## 7  0.2049   0.04267  0.04249 -0.03235  0.04249 0.04249 -0.01467  0.008731 114.5
## 8  0.2680   0.04280  0.04249  0.007679 0.04249 0.04249  0.003481 0.008731 114.5
##   df_DD n_covariates_DD adj_fct_DD adj_value_DD gt_DD_pop vgt_DD_pop Wgt_DD_pop
##   <dbl>           <dbl> <chr>             <dbl>     <dbl>      <dbl>      <dbl>
## 1 130.0               1 eta               1.291 NA          NA         NA      
## 2 130.0               1 eta               1.291 NA          NA         NA      
## 3 130.0               1 eta               1.291 NA          NA         NA      
## 4 130.0               1 eta               1.291 -0.2134      0.03615    0.03604
## 5 114.5               1 eta               1.283 NA          NA         NA      
## 6 114.5               1 eta               1.283 NA          NA         NA      
## 7 114.5               1 eta               1.283 NA          NA         NA      
## 8 114.5               1 eta               1.283  0.007589    0.04060    0.04060
##   vary_id  
##   <chr>    
## 1 NEL/9m   
## 2 BSI/9m   
## 3 MANSA/9m 
## 4 GAF/9m   
## 5 NEL/18m  
## 6 BSI/18m  
## 7 MANSA/18m
## 8 GAF/18m

Valiente et al. (2022) (Quality-checked)

Entering data from Table 3 and 4 (p. 4)

# Data from Table 3 and Table  4 (p. 4)  containing means, standard deviation. It also contains
# difference Morris' d and p

Valiente2022  <- tibble(
  group = rep(c("Multicomponent PPI", # Intervention
                "Treatment as usual"), # Control
              each = 1,16),
  
  outcome = rep(c(
    # Table 3
    "SPWB Autonomy",          #Scales of Psychological Well-Being (SPWB)
    "SPWB Positive_relationship", 
    "SPWB Self_acceptance",
    "SPWB Enviormental mastery",
    "SPWB Purpose_in_life",
    "SPWB Personal_Growth",
    "SWLS",                  # Satisfaction With Life Scale (SWLS)
    
    # Table 4
    "Somatization",         # Symptom checklist-90-Revised (SCL-90-R)
    "Obsessive–compulsive", 
    "Interpersonal sensibility",
    "Depression", 
    "Anxiety", 
    "Hostility", 
    "Phobic anxiety",
    "Paranoid ideation", 
    "Psychoticism"
    
  ), 
  each = 2,1),
  
    
  N_start = rep(c(71, 70), n_distinct(outcome)),
  
  N = rep(c(52,61),
          each = 1,16),
  
  m_pre = c(
    
    # Table 3
    19.5, 20.1,
    34.7, 33.9,
    29.9, 31.6,
    30.6, 31.4,
    34.4, 34.4,
    35.3, 35.7,
    17.4, 18.2,
    
    # Table 4
    1.00, 1.02,
    1.66, 1.62,
    1.42, 1.41,
    1.51, 1.47,
    1.12, 1.31,
    0.75, 0.63,
    0.89, 1.09,
    1.15, 1.15,
    1.14, 0.99
    
  ),
  
  sd_pre = c(
    # Table 3
    4.4,  4.2,
    8.0, 7.5,
    8.7, 7.8,
    7.8, 7.1,
    7.7, 6.6,
    7.4, 7.2,
    7.1, 6.8,
    
    # Table 4
    0.83, 0.81,
    0.87, 0.93,
    0.81, 0.95,
    0.87, 0.89,
    0.82, 0.97,
    0.83, 0.80,
    0.77, 1.01,
    0.86, 0.86,
    0.82, 0.83
    
  ),
  
  
  m_post = c(
    # Table 3
    19.9, 19.7,
    35.7, 34.4,
    31.7, 31.1,
    32.7, 30.8,
    34.9, 34.2,
    35.5, 35.5,
    19.0, 19.2,
    
    # Table 4
    0.90, 0.96,
    1.50, 1.48,
    1.28, 1.27,
    1.30, 1.40,
    1.07, 1.14,
    0.71, 0.61,
    0.91, 1.07,
    1.18, 1.21,
    0.99, 0.98
  ),
  
  sd_post = c(
    # Table 3
    3.4, 4.2, 
    7.1, 7.6, 
    7.5, 7.8, 
    7.5, 7.3, 
    6.9, 5.9,
    6.8, 6.8,
    6.6, 6.8,
    
    # Table 4
    0.82, 0.84,
    0.86, 0.96,
    0.79, 0.89,
    0.80, 0.94,
    0.84, 0.91,
    0.76, 0.79,
    0.80, 0.97,
    0.90, 0.87,
    0.85, 0.86
  )
  
)

# Making the tibble wide 

Valiente2022_wide <-
  Valiente2022 |> 
  mutate (group = case_match(
    group, "Multicomponent PPI"  ~ "t", "Treatment as usual" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  )

# Effect sizes: Estimating G and D (pre-post test) (Not finished yest)
Valiente2022_est <-           
  Valiente2022_wide |>
  mutate(
    analysis_plan = rep(c(
      "Wellbeing and Quality of Life",
      "Wellbeing and Quality of Life",
      "All mental health outcomes"
    ), c(6,1,9)),
    
    study = "Valiente et al. 2022",
    
  effect_size = "SMD",
   sd_used = "Pooled posttest SD",
   main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp, 
    ppcor_method = "Imputed"
    
    
  )|> 
  mutate(
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # The outcome SCL-90-R reverted because lower score is beneficial
    m_post = if_else(analysis_plan != "All mental health outcomes", m_post_t - m_post_c,
                     (m_post_t - m_post_c) * -1),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For BSI: Lower is beneficial
    m_diff_t = if_else(analysis_plan != "All mental health outcomes", m_post_t - m_post_c, (m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(analysis_plan != "All mental health outcomes", m_post_c - m_pre_c, (m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # Group size is imputed
    
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Valiente2022_est
## # A tibble: 16 × 61
##    outcome                    N_start_t N_start_c   N_t   N_c m_pre_t m_pre_c
##    <chr>                          <dbl>     <dbl> <dbl> <dbl>   <dbl>   <dbl>
##  1 SPWB Autonomy                     71        70    52    61   19.5    20.1 
##  2 SPWB Positive_relationship        71        70    52    61   34.7    33.9 
##  3 SPWB Self_acceptance              71        70    52    61   29.9    31.6 
##  4 SPWB Enviormental mastery         71        70    52    61   30.6    31.4 
##  5 SPWB Purpose_in_life              71        70    52    61   34.4    34.4 
##  6 SPWB Personal_Growth              71        70    52    61   35.3    35.7 
##  7 SWLS                              71        70    52    61   17.4    18.2 
##  8 Somatization                      71        70    52    61    1       1.02
##  9 Obsessive–compulsive              71        70    52    61    1.66    1.62
## 10 Interpersonal sensibility         71        70    52    61    1.42    1.41
## 11 Depression                        71        70    52    61    1.51    1.47
## 12 Anxiety                           71        70    52    61    1.12    1.31
## 13 Hostility                         71        70    52    61    0.75    0.63
## 14 Phobic anxiety                    71        70    52    61    0.89    1.09
## 15 Paranoid ideation                 71        70    52    61    1.15    1.15
## 16 Psychoticism                      71        70    52    61    1.14    0.99
##    sd_pre_t sd_pre_c m_post_t m_post_c sd_post_t sd_post_c
##       <dbl>    <dbl>    <dbl>    <dbl>     <dbl>     <dbl>
##  1     4.4      4.2     19.9     19.7       3.4       4.2 
##  2     8        7.5     35.7     34.4       7.1       7.6 
##  3     8.7      7.8     31.7     31.1       7.5       7.8 
##  4     7.8      7.1     32.7     30.8       7.5       7.3 
##  5     7.7      6.6     34.9     34.2       6.9       5.9 
##  6     7.4      7.2     35.5     35.5       6.8       6.8 
##  7     7.1      6.8     19       19.2       6.6       6.8 
##  8     0.83     0.81     0.9      0.96      0.82      0.84
##  9     0.87     0.93     1.5      1.48      0.86      0.96
## 10     0.81     0.95     1.28     1.27      0.79      0.89
## 11     0.87     0.89     1.3      1.4       0.8       0.94
## 12     0.82     0.97     1.07     1.14      0.84      0.91
## 13     0.83     0.8      0.71     0.61      0.76      0.79
## 14     0.77     1.01     0.91     1.07      0.8       0.97
## 15     0.86     0.86     1.18     1.21      0.9       0.87
## 16     0.82     0.83     0.99     0.98      0.85      0.86
##    analysis_plan                 study                effect_size
##    <chr>                         <chr>                <chr>      
##  1 Wellbeing and Quality of Life Valiente et al. 2022 SMD        
##  2 Wellbeing and Quality of Life Valiente et al. 2022 SMD        
##  3 Wellbeing and Quality of Life Valiente et al. 2022 SMD        
##  4 Wellbeing and Quality of Life Valiente et al. 2022 SMD        
##  5 Wellbeing and Quality of Life Valiente et al. 2022 SMD        
##  6 Wellbeing and Quality of Life Valiente et al. 2022 SMD        
##  7 Wellbeing and Quality of Life Valiente et al. 2022 SMD        
##  8 All mental health outcomes    Valiente et al. 2022 SMD        
##  9 All mental health outcomes    Valiente et al. 2022 SMD        
## 10 All mental health outcomes    Valiente et al. 2022 SMD        
## 11 All mental health outcomes    Valiente et al. 2022 SMD        
## 12 All mental health outcomes    Valiente et al. 2022 SMD        
## 13 All mental health outcomes    Valiente et al. 2022 SMD        
## 14 All mental health outcomes    Valiente et al. 2022 SMD        
## 15 All mental health outcomes    Valiente et al. 2022 SMD        
## 16 All mental health outcomes    Valiente et al. 2022 SMD        
##    sd_used            main_es_method    ppcor ppcor_method N_total df_ind
##    <chr>              <chr>             <dbl> <chr>          <dbl>  <dbl>
##  1 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
##  2 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
##  3 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
##  4 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
##  5 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
##  6 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
##  7 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
##  8 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
##  9 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
## 10 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
## 11 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
## 12 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
## 13 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
## 14 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
## 15 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
## 16 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed          113    113
##      m_post sd_pool   d_post vd_post Wd_post      J   g_post vg_post Wg_post
##       <dbl>   <dbl>    <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl>   <dbl>
##  1  0.2000   3.853   0.05191 0.03564 0.03562 0.9933  0.05156 0.03564 0.03562
##  2  1.300    7.374   0.1763  0.03576 0.03562 0.9933  0.1751  0.03576 0.03562
##  3  0.6000   7.664   0.07829 0.03565 0.03562 0.9933  0.07777 0.03565 0.03562
##  4  1.900    7.393   0.2570  0.03592 0.03562 0.9933  0.2553  0.03591 0.03562
##  5  0.7000   6.379   0.1097  0.03568 0.03562 0.9933  0.1090  0.03568 0.03562
##  6  0        6.8     0       0.03562 0.03562 0.9933  0       0.03562 0.03562
##  7 -0.2000   6.709  -0.02981 0.03563 0.03562 0.9933 -0.02961 0.03563 0.03562
##  8  0.06000  0.8309  0.07221 0.03565 0.03562 0.9933  0.07173 0.03565 0.03562
##  9 -0.02000  0.9154 -0.02185 0.03563 0.03562 0.9933 -0.02170 0.03563 0.03562
## 10 -0.01000  0.8455 -0.01183 0.03562 0.03562 0.9933 -0.01175 0.03562 0.03562
## 11  0.1000   0.8785  0.1138  0.03568 0.03562 0.9933  0.1131  0.03568 0.03562
## 12  0.07000  0.8785  0.07968 0.03565 0.03562 0.9933  0.07915 0.03565 0.03562
## 13 -0.1      0.7764 -0.1288  0.03570 0.03562 0.9933 -0.1279  0.03570 0.03562
## 14  0.16     0.8959  0.1786  0.03577 0.03562 0.9933  0.1774  0.03576 0.03562
## 15  0.03000  0.8839  0.03394 0.03563 0.03562 0.9933  0.03371 0.03563 0.03562
## 16 -0.01000  0.8554 -0.01169 0.03562 0.03562 0.9933 -0.01161 0.03562 0.03562
##    m_diff_t m_diff_c     d_DD   vd_DD   Wd_DD     g_DD   vg_DD   Wg_DD
##       <dbl>    <dbl>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
##  1  0.2000  -0.4000   0.1557  0.03573 0.03562  0.1547  0.03573 0.03562
##  2  1.300    0.5      0.1085  0.03568 0.03562  0.1078  0.03568 0.03562
##  3  0.6000  -0.5      0.1435  0.03572 0.03562  0.1426  0.03571 0.03562
##  4  1.900   -0.6000   0.3382  0.03613 0.03562  0.3359  0.03612 0.03562
##  5  0.7000  -0.2000   0.1411  0.03571 0.03562  0.1402  0.03571 0.03562
##  6  0       -0.2000   0.02941 0.03563 0.03562  0.02922 0.03563 0.03562
##  7 -0.2000   1       -0.1789  0.03577 0.03562 -0.1777  0.03576 0.03562
##  8  0.1      0.06000  0.04814 0.03563 0.03562  0.04782 0.03563 0.03562
##  9  0.1600   0.1400   0.02185 0.03563 0.03562  0.02170 0.03563 0.03562
## 10  0.1400   0.1400   0       0.03562 0.03562  0       0.03562 0.03562
## 11  0.21     0.07000  0.1594  0.03574 0.03562  0.1583  0.03574 0.03562
## 12  0.05000  0.1700  -0.1366  0.03571 0.03562 -0.1357  0.03571 0.03562
## 13  0.04000  0.02000  0.02576 0.03563 0.03562  0.02559 0.03563 0.03562
## 14 -0.02000  0.02000 -0.04465 0.03563 0.03562 -0.04435 0.03563 0.03562
## 15 -0.03000 -0.06000  0.03394 0.03563 0.03562  0.03371 0.03563 0.03562
## 16  0.1500   0.01000  0.1637  0.03574 0.03562  0.1626  0.03574 0.03562
##    avg_cl_size avg_cl_type   icc icc_type n_covariates gamma_sqrt df_adj  omega
##          <dbl> <chr>       <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
##  1           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
##  2           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
##  3           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
##  4           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
##  5           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
##  6           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
##  7           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
##  8           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
##  9           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
## 10           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
## 11           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
## 12           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
## 13           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
## 14           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
## 15           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
## 16           8 Imputed       0.1 Imputed             1      0.969  107.3 0.9930
##     gt_post vgt_post Wgt_post    gt_DD  vgt_DD  Wgt_DD     hg_DD   vhg_DD  h_DD
##       <dbl>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>     <dbl>    <dbl> <dbl>
##  1  0.04994  0.04746  0.04745  0.1498  0.04756 0.04745  0.06637  0.009316 107.3
##  2  0.1696   0.04759  0.04745  0.1044  0.04750 0.04745  0.04624  0.009316 107.3
##  3  0.07533  0.04748  0.04745  0.1381  0.04754 0.04745  0.06118  0.009316 107.3
##  4  0.2473   0.04774  0.04745  0.3254  0.04794 0.04745  0.1439   0.009316 107.3
##  5  0.1056   0.04750  0.04745  0.1358  0.04754 0.04745  0.06014  0.009316 107.3
##  6  0        0.04745  0.04745  0.02830 0.04746 0.04745  0.01254  0.009316 107.3
##  7 -0.02868  0.04746  0.04745 -0.1721  0.04759 0.04745 -0.07622  0.009316 107.3
##  8  0.06948  0.04747  0.04745  0.04632 0.04746 0.04745  0.02052  0.009316 107.3
##  9 -0.02102  0.04745  0.04745  0.02102 0.04745 0.04745  0.009315 0.009316 107.3
## 10 -0.01138  0.04745  0.04745  0       0.04745 0.04745  0        0.009316 107.3
## 11  0.1095   0.04751  0.04745  0.1533  0.04756 0.04745  0.06792  0.009316 107.3
## 12  0.07667  0.04748  0.04745 -0.1314  0.04753 0.04745 -0.05822  0.009316 107.3
## 13 -0.1239   0.04752  0.04745  0.02479 0.04745 0.04745  0.01098  0.009316 107.3
## 14  0.1718   0.04759  0.04745 -0.04296 0.04746 0.04745 -0.01903  0.009316 107.3
## 15  0.03266  0.04746  0.04745  0.03266 0.04746 0.04745  0.01447  0.009316 107.3
## 16 -0.01125  0.04745  0.04745  0.1575  0.04757 0.04745  0.06975  0.009316 107.3
##    df_DD n_covariates_DD adj_fct_DD adj_value_DD vary_id                   
##    <dbl>           <dbl> <chr>             <dbl> <chr>                     
##  1 107.3               1 eta               1.332 SPWB Autonomy             
##  2 107.3               1 eta               1.332 SPWB Positive_relationship
##  3 107.3               1 eta               1.332 SPWB Self_acceptance      
##  4 107.3               1 eta               1.332 SPWB Enviormental mastery 
##  5 107.3               1 eta               1.332 SPWB Purpose_in_life      
##  6 107.3               1 eta               1.332 SPWB Personal_Growth      
##  7 107.3               1 eta               1.332 SWLS                      
##  8 107.3               1 eta               1.332 Somatization              
##  9 107.3               1 eta               1.332 Obsessive–compulsive      
## 10 107.3               1 eta               1.332 Interpersonal sensibility 
## 11 107.3               1 eta               1.332 Depression                
## 12 107.3               1 eta               1.332 Anxiety                   
## 13 107.3               1 eta               1.332 Hostility                 
## 14 107.3               1 eta               1.332 Phobic anxiety            
## 15 107.3               1 eta               1.332 Paranoid ideation         
## 16 107.3               1 eta               1.332 Psychoticism

Volpe et al. (2015) (Quality-checked)

Entering data from Table 2 (p. 19)

Volpe_2015 <- tibble(
  # Taken from table 2, p. 19
  outcome = rep(c(
    "BPRS",   # Brief Psychiatric Rating Scale
    "PHQ-9",  # Patient Health Questionnaire
    "PSP",    # Personal and Social Performance Scale
    "DAS-II"  # Disability Assessment Schedule 2.0
  ), each = 2, 1), 
  
  
  group = rep(c(
    "Reading group",
    "Control group"
  ), each = 1, 4), 
  
  N_start = rep(c(25)),

  N = rep(c(21,20), each = 1, 4), 

  m_pre = c(
    49.95, 51.2, # Brief Psychiatric Rating Scale
    9.74, 9.9,   # Patient Health Questionnaire
    44.09, 46.2, # Personal and Social Performance Scale
    2.64, 2.95   # Disability Assessment Schedule 2.0
  ),
  
  sd_pre =c(
    15.48, 17.75,
    4.58, 4.35,
    16.19, 18.23,
    0.93, 0.94
  ),
  
  m_post = c(
    36.21, 41.2,
    4.7, 5.66,
    49.95, 47.25,
    1.25, 2.13
  ), 
  
  sd_post = c(
    17.43, 15.69,
    2.78, 2.45,
    15.53, 18.35,
    0.5, 0.25
  )
)



# Making the tibble into wideformat
Volpe2015_est <-
  Volpe_2015 |> 
  mutate(group = case_match(group, "Reading group" ~ "t", "Control group" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  ) |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "BPRS") ~ "All mental health outcomes",
      str_detect(outcome, "PHQ-9") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "PSP|DAS-II") ~ "Social functioning (degree of impairment)"),
    
    
    study = "Volpe et al. 2015",
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    
    # ppcor imputed
    ppcor = ppcor_imp, 
    ppcor_method = "Imputed",
    
    
    # MANOVA estimates obtained from raw text on page 18 for between-group comparisons. 
    # But the effect-sizes seems wrong when using them. 
    #  F_val = c(NA_real_, 
    #            NA_real_,
    #            7.29,
    #            3.07),
    
    #  df1 = c(NA_real_,
    #          NA_real_,
    #          1, 
    #          1),
    
    #  df2 = c(NA_real_,
    #          NA_real_,
    #          39,
    #          39),
    
    #  p_val_F = c(NA_real_,
    #              NA_real_,
    #              0.008,
    #              0.005
    #  ),
    
    N_total = N_t + N_c,
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    # For BPRS, PHQ-9, and DAs-II lower scores are beneficial why these is reverted
    m_post = if_else(outcome != "PSP", (m_post_t - m_post_c)*-1, m_post_t - m_post_c),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For BPRS, PHQ-9, and DAs-II lower scores are beneficial why these is reverted
    m_diff_t = if_else(outcome != "PSP", (m_post_t - m_pre_t)*-1, m_post_t - m_post_c),
    m_diff_c = if_else(outcome != "PSP", (m_post_c - m_pre_c)*-1, m_post_c - m_pre_c), 
    
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # Group size is imputed
    
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "9"),
      omega * ((m_diff_t - m_diff_c)/PHQ_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*PHQ_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Volpe2015_est
## # A tibble: 4 × 64
##   outcome N_start_t N_start_c   N_t   N_c m_pre_t m_pre_c sd_pre_t sd_pre_c
##   <chr>       <dbl>     <dbl> <dbl> <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 BPRS           25        25    21    20   49.95   51.2     15.48    17.75
## 2 PHQ-9          25        25    21    20    9.74    9.9      4.58     4.35
## 3 PSP            25        25    21    20   44.09   46.2     16.19    18.23
## 4 DAS-II         25        25    21    20    2.64    2.95     0.93     0.94
##   m_post_t m_post_c sd_post_t sd_post_c
##      <dbl>    <dbl>     <dbl>     <dbl>
## 1    36.21    41.2      17.43     15.69
## 2     4.7      5.66      2.78      2.45
## 3    49.95    47.25     15.53     18.35
## 4     1.25     2.13      0.5       0.25
##   analysis_plan                             study             effect_size
##   <chr>                                     <chr>             <chr>      
## 1 All mental health outcomes                Volpe et al. 2015 SMD        
## 2 All mental health outcomes/Depression     Volpe et al. 2015 SMD        
## 3 Social functioning (degree of impairment) Volpe et al. 2015 SMD        
## 4 Social functioning (degree of impairment) Volpe et al. 2015 SMD        
##   sd_used            main_es_method    ppcor ppcor_method N_total df_ind m_post
##   <chr>              <chr>             <dbl> <chr>          <dbl>  <dbl>  <dbl>
## 1 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           41     41  4.99 
## 2 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           41     41  0.96 
## 3 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           41     41  2.700
## 4 Pooled posttest SD Raw diff-in-diffs   0.5 Imputed           41     41  0.88 
##   sd_pool d_post vd_post Wd_post      J g_post vg_post Wg_post m_diff_t m_diff_c
##     <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
## 1 16.61   0.3005 0.09872 0.09762 0.9816 0.2950 0.09868 0.09762   13.74    10    
## 2  2.624  0.3658 0.09925 0.09762 0.9816 0.3591 0.09919 0.09762    5.04     4.24 
## 3 16.96   0.1592 0.09793 0.09762 0.9816 0.1562 0.09792 0.09762    2.700    1.050
## 4  0.3983 2.209  0.1571  0.09762 0.9816 2.169  0.1550  0.09762    1.39     0.82 
##      d_DD   vd_DD   Wd_DD    g_DD   vg_DD   Wg_DD avg_cl_size avg_cl_type   icc
##     <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>       <dbl> <chr>       <dbl>
## 1 0.2252  0.09824 0.09762 0.2211  0.09822 0.09762           8 Imputed       0.1
## 2 0.3048  0.09875 0.09762 0.2992  0.09871 0.09762           8 Imputed       0.1
## 3 0.09727 0.09773 0.09762 0.09548 0.09773 0.09762           8 Imputed       0.1
## 4 1.431   0.1226  0.09762 1.405   0.1217  0.09762           8 Imputed       0.1
##   icc_type n_covariates gamma_sqrt df_adj  omega gt_post vgt_post Wgt_post
##   <chr>           <dbl>      <dbl>  <dbl>  <dbl>   <dbl>    <dbl>    <dbl>
## 1 Imputed             1      0.966  37.89 0.9801  0.2845   0.1270   0.1259
## 2 Imputed             1      0.966  37.89 0.9801  0.3463   0.1275   0.1259
## 3 Imputed             1      0.966  37.89 0.9801  0.1507   0.1262   0.1259
## 4 Imputed             1      0.966  37.89 0.9801  2.092    0.1837   0.1259
##     gt_DD vgt_DD Wgt_DD   hg_DD  vhg_DD  h_DD df_DD n_covariates_DD adj_fct_DD
##     <dbl>  <dbl>  <dbl>   <dbl>   <dbl> <dbl> <dbl>           <dbl> <chr>     
## 1 0.2132  0.1265 0.1259 0.09754 0.02639 37.89 37.89               1 eta       
## 2 0.2886  0.1270 0.1259 0.1319  0.02639 37.89 37.89               1 eta       
## 3 0.09209 0.1260 0.1259 0.04215 0.02639 37.89 37.89               1 eta       
## 4 1.355   0.1502 0.1259 0.6019  0.02639 37.89 37.89               1 eta       
##   adj_value_DD gt_DD_pop vgt_DD_pop Wgt_DD_pop vary_id
##          <dbl>     <dbl>      <dbl>      <dbl> <chr>  
## 1         1.29   NA         NA         NA      BPRS   
## 2         1.29    0.1251     0.1242     0.1241 PHQ-9  
## 3         1.29   NA         NA         NA      PSP    
## 4         1.29   NA         NA         NA      DAS-II

Wojtalik et al. (2019) (Quality-checked)

Entering data from Table 2 and 3 (p. 505)

# Data from table 2 and table (p. 505)  containing marginal means with standard error
# It also contains standard error. To calculate pre-post test we will use the standard 
# deviation from table 1 (p. 504)

Wojtalik2019 <- tibble( 
  group = as.factor(rep(c("Cognitive enhancement therapy (CET)", # Intervention
                          "Enriched supportive therapy (EST)"), # Control
                        each = 1,4)),
  
  analysis = rep(c("ITT", "Completers"), each = 4),
  
  
 # timing = 18,
  
  outcome = as.factor(rep(c(
    #      "Cognition composite",
    "Social adjustment composite",
    "Symptoms composite"), 
    each = 2,2)),
  
   N_start = rep(c(
    58, 44), each = 1,4),
 
  N = c(rep(c(58, 44), each = 1,2),
        rep(c(26, 23),each = 1,2)),
  
  
  b_emm_pre = c(
    # ITT
    #    34.4, 38,  # Baseline Cognition composite 
    49.5, 51.0,# Baseline Social adjustment composite
    50.2, 50.4, # Baseline Symptoms composite
    
    
    # Completers
    
    #    34.6, 39.4, # Baseline Cognition composite
    47.1, 50.4, # Baseline Social adjustment composite
    49.4, 50.2 # Baseline Symptoms composite
    
  ),
  
  # Taken from table 1 (p. 504)
  sd_pre = rep(
    c(
      # ITT
      #    11.3, 11.8, # Baseline Cognition composite  
      9.6, 10.5,  # Baseline Social adjustment composite
      10.4, 10.0), 2
  
    # Baseline Symptoms composite
  
  # Completer (taken from online supplements)
  #rep(c(
  #  #    12.52, 12.52,
  #  8.80, 8.80,
  #  9.18, 9.18
  #), each = 1,1)
  ),
  
  se_emm_pre = rep(c(
    # ITT
    #    1.5, 1.7,  # Baseline Cognition composite 
    1.3, 1.4,  # Baseline Social adjustment composite
    1.3, 1.5,   # Baseline Symptoms composite
    
    # Completer
    #    2.4, 2.6,
    1.8, 1.8,
    1.9, 2.0
    
  ), each = 1,1),
  
  b_emm_post = c(
    # ITT
    #    38.4, 37.1, # 9 months Cognition composite
    #    54.1, 56.8, # 9 months Social adjustment composite
    #    53.5, 53.4, # 9 months Symptoms composite
    
    #    40.1, 39.8, # 18 months Cognition composite
    56.3, 55.5, # 18 months Social adjustment composite
    54.3, 53.5,  # 18 months Symptoms composite
    
    # Completer
    #    40.0, 37.5, # 9 months Cognition composite
    #    52.2, 54.9, # 9 months Social adjustment composite
    #    53.2, 52.8, # 9 months Symptoms composite
    
    #    41.3, 40.6, # 18 months Cognition composite
    54.5, 53.4, # 18 months Social adjustment composite
    54.0, 53.1  # 18 months Symptoms composite
    
  ),
  
  se_emm_post = c(
    # ITT
    #    1.6, 1.9, # 9 months Cognition composite
    #    1.4, 1.7, # 9 months Social adjustment composite
    #    1.4, 1.7, # 9 months Symptoms composite
    
    #    1.8, 2.0, # 18 months Cognition composite
    2.0, 2.3, # 18 months Social adjustment composite
    1.8, 1.9,  # 18 months Symptoms composite
    
    # Completer
    #    2.4, 2.6, # 9 months Cognition composite
    #    1.9, 2.0, # 9 months Social adjustment composite
    #    1.8, 1.9, # 9 months Symptoms composite
    
    #    2.5, 2.6, # 18 months Cognition composite
    2.4, 2.5, # 18 months Social adjustment composite
    2.0, 2.1  # 18 months Symptoms composite
    
  )
  
); Wojtalik2019 
## # A tibble: 8 × 10
##   group                               analysis   outcome                    
##   <fct>                               <chr>      <fct>                      
## 1 Cognitive enhancement therapy (CET) ITT        Social adjustment composite
## 2 Enriched supportive therapy (EST)   ITT        Social adjustment composite
## 3 Cognitive enhancement therapy (CET) ITT        Symptoms composite         
## 4 Enriched supportive therapy (EST)   ITT        Symptoms composite         
## 5 Cognitive enhancement therapy (CET) Completers Social adjustment composite
## 6 Enriched supportive therapy (EST)   Completers Social adjustment composite
## 7 Cognitive enhancement therapy (CET) Completers Symptoms composite         
## 8 Enriched supportive therapy (EST)   Completers Symptoms composite         
##   N_start     N b_emm_pre sd_pre se_emm_pre b_emm_post se_emm_post
##     <dbl> <dbl>     <dbl>  <dbl>      <dbl>      <dbl>       <dbl>
## 1      58    58      49.5    9.6        1.3       56.3         2  
## 2      44    44      51     10.5        1.4       55.5         2.3
## 3      58    58      50.2   10.4        1.3       54.3         1.8
## 4      44    44      50.4   10          1.5       53.5         1.9
## 5      58    26      47.1    9.6        1.8       54.5         2.4
## 6      44    23      50.4   10.5        1.8       53.4         2.5
## 7      58    26      49.4   10.4        1.9       54           2  
## 8      44    23      50.2   10          2         53.1         2.1
# Making the tibble into wide format
Wojtalik2019_wide <- Wojtalik2019 |> 
  mutate(
    group = case_match(
      group, 
      "Cognitive enhancement therapy (CET)" ~ "t", 
      "Enriched supportive therapy (EST)" ~ "c"
    )
  ) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from =  N_start:last_col()
  ) |> 
  mutate(
    t_val = c(0.8, 0.43, 1.60, 0.60),
    es_paper = c(0.23, 0.11, 0.51, 0.18),
    es_paper_type = "Glass' delta (assumed)"
  
  ); Wojtalik2019_wide
## # A tibble: 4 × 19
##   analysis   outcome                     N_start_t N_start_c   N_t   N_c
##   <chr>      <fct>                           <dbl>     <dbl> <dbl> <dbl>
## 1 ITT        Social adjustment composite        58        44    58    44
## 2 ITT        Symptoms composite                 58        44    58    44
## 3 Completers Social adjustment composite        58        44    26    23
## 4 Completers Symptoms composite                 58        44    26    23
##   b_emm_pre_t b_emm_pre_c sd_pre_t sd_pre_c se_emm_pre_t se_emm_pre_c
##         <dbl>       <dbl>    <dbl>    <dbl>        <dbl>        <dbl>
## 1        49.5        51        9.6     10.5          1.3          1.4
## 2        50.2        50.4     10.4     10            1.3          1.5
## 3        47.1        50.4      9.6     10.5          1.8          1.8
## 4        49.4        50.2     10.4     10            1.9          2  
##   b_emm_post_t b_emm_post_c se_emm_post_t se_emm_post_c t_val es_paper
##          <dbl>        <dbl>         <dbl>         <dbl> <dbl>    <dbl>
## 1         56.3         55.5           2             2.3  0.8      0.23
## 2         54.3         53.5           1.8           1.9  0.43     0.11
## 3         54.5         53.4           2.4           2.5  1.6      0.51
## 4         54           53.1           2             2.1  0.6      0.18
##   es_paper_type         
##   <chr>                 
## 1 Glass' delta (assumed)
## 2 Glass' delta (assumed)
## 3 Glass' delta (assumed)
## 4 Glass' delta (assumed)
Wojtalik2019_est <- 
  Wojtalik2019_wide |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "Social adjustment composite") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "Symptoms composite") ~ "All mental health outcomes"),
    
    study = "Wojtalik et al. 2019", 
    
    effect_size = "SMD",
    sd_used = "Pooled pretest SD - Posttest SD not reported",
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor_method = "Not computable - t values used"
    
  ) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    mean_diff = b_emm_post_t - b_emm_post_c,
    sd_pool = sqrt(((N_t-1)*sd_pre_t^2 + (N_c-1)*sd_pre_c^2)/(N_t + N_c - 2)),
      
    d_adj = mean_diff/sd_pool,
    vd_adj = d_adj^2/t_val^2 + d_adj^2/(2*df_ind),
    Wd_adj = d_adj^2/t_val^2,
    
    J = 1 - 3/(4*df_ind-1),
    
    g_adj = J * d_adj,
    vd_adj = g_adj^2/t_val^2 + g_adj^2/(2*df_ind),
    Wg_adj = g_adj^2/t_val^2
    
    
  ) |> 
  rowwise() |> 
  mutate(
    avg_cl_size = 4, 
    avg_cl_type = "From study (p. 530)", 
    
    icc = ICC_01, 
    icc_type = "Imputed", 
    n_covariates = 1, 
    
    gamma_sqrt = gamma_1armcluster(N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc), 
    
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size), 
    
    omega = 1 - 3 / (4 * df_adj - 1), 
    
    gt_adj = omega * d_adj * gamma_sqrt, 
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_adj, 
      model = "emmeans", 
      cluster_adj = FALSE, 
      t_val = t_val, 
      q = n_covariates, 
      add_name_to_vars = "_adj", 
      vars = -c("var_term1_adj")
      ),
    
    vary_id = paste0(outcome, "/", analysis)
  ) |>  
  ungroup(); Wojtalik2019_est
## # A tibble: 4 × 53
##   analysis   outcome                     N_start_t N_start_c   N_t   N_c
##   <chr>      <fct>                           <dbl>     <dbl> <dbl> <dbl>
## 1 ITT        Social adjustment composite        58        44    58    44
## 2 ITT        Symptoms composite                 58        44    58    44
## 3 Completers Social adjustment composite        58        44    26    23
## 4 Completers Symptoms composite                 58        44    26    23
##   b_emm_pre_t b_emm_pre_c sd_pre_t sd_pre_c se_emm_pre_t se_emm_pre_c
##         <dbl>       <dbl>    <dbl>    <dbl>        <dbl>        <dbl>
## 1        49.5        51        9.6     10.5          1.3          1.4
## 2        50.2        50.4     10.4     10            1.3          1.5
## 3        47.1        50.4      9.6     10.5          1.8          1.8
## 4        49.4        50.2     10.4     10            1.9          2  
##   b_emm_post_t b_emm_post_c se_emm_post_t se_emm_post_c t_val es_paper
##          <dbl>        <dbl>         <dbl>         <dbl> <dbl>    <dbl>
## 1         56.3         55.5           2             2.3  0.8      0.23
## 2         54.3         53.5           1.8           1.9  0.43     0.11
## 3         54.5         53.4           2.4           2.5  1.6      0.51
## 4         54           53.1           2             2.1  0.6      0.18
##   es_paper_type          analysis_plan                            
##   <chr>                  <chr>                                    
## 1 Glass' delta (assumed) Social functioning (degree of impairment)
## 2 Glass' delta (assumed) All mental health outcomes               
## 3 Glass' delta (assumed) Social functioning (degree of impairment)
## 4 Glass' delta (assumed) All mental health outcomes               
##   study                effect_size sd_used                                     
##   <chr>                <chr>       <chr>                                       
## 1 Wojtalik et al. 2019 SMD         Pooled pretest SD - Posttest SD not reported
## 2 Wojtalik et al. 2019 SMD         Pooled pretest SD - Posttest SD not reported
## 3 Wojtalik et al. 2019 SMD         Pooled pretest SD - Posttest SD not reported
## 4 Wojtalik et al. 2019 SMD         Pooled pretest SD - Posttest SD not reported
##   main_es_method    ppcor_method                   N_total df_ind mean_diff
##   <chr>             <chr>                            <dbl>  <dbl>     <dbl>
## 1 Raw diff-in-diffs Not computable - t values used     102    102    0.8000
## 2 Raw diff-in-diffs Not computable - t values used     102    102    0.8000
## 3 Raw diff-in-diffs Not computable - t values used      49     49    1.100 
## 4 Raw diff-in-diffs Not computable - t values used      49     49    0.9000
##   sd_pool   d_adj   vd_adj   Wd_adj      J   g_adj   Wg_adj avg_cl_size
##     <dbl>   <dbl>    <dbl>    <dbl>  <dbl>   <dbl>    <dbl>       <dbl>
## 1   9.997 0.08002 0.009890 0.01001  0.9926 0.07943 0.009859           4
## 2  10.23  0.07820 0.03262  0.03307  0.9926 0.07763 0.03259            4
## 3  10.03  0.1097  0.004673 0.004697 0.9846 0.1080  0.004554           4
## 4  10.21  0.08811 0.02098  0.02156  0.9846 0.08675 0.02091            4
##   avg_cl_type           icc icc_type n_covariates gamma_sqrt df_adj  omega
##   <chr>               <dbl> <chr>           <dbl>      <dbl>  <dbl>  <dbl>
## 1 From study (p. 530)   0.1 Imputed             1      0.977  97.99 0.9923
## 2 From study (p. 530)   0.1 Imputed             1      0.977  97.99 0.9923
## 3 From study (p. 530)   0.1 Imputed             1      0.973  46.15 0.9837
## 4 From study (p. 530)   0.1 Imputed             1      0.973  46.15 0.9837
##    gt_adj  vgt_adj  Wgt_adj  hg_adj vhg_adj h_adj n_covariates_adj adj_fct_adj
##     <dbl>    <dbl>    <dbl>   <dbl>   <dbl> <dbl>            <dbl> <chr>      
## 1 0.07758 0.01012  0.01009  0.07798 0.01021 97.99                1 eta        
## 2 0.07582 0.03339  0.03336  0.04193 0.01021 97.99                1 eta        
## 3 0.1050  0.004801 0.004681 0.2248  0.02167 46.15                1 eta        
## 4 0.08433 0.02157  0.02149  0.08462 0.02167 46.15                1 eta        
##   adj_value_adj vary_id                               
##           <dbl> <chr>                                 
## 1         1.073 Social adjustment composite/ITT       
## 2         1.073 Symptoms composite/ITT                
## 3         1.088 Social adjustment composite/Completers
## 4         1.088 Symptoms composite/Completers

Wuthrich & Rapee (2013) and Smith et al. (2021) (Quality-checked)

Entering data from Table 2 (p. 445). Sample size information is retrieved from Table 1 (p. 442).

# Calculate paired t-test statistics to obtain pre-posttest score correlation estimates

#-----------------------------------------------------------------
# OUTCOME ANCRONYMS AND FULL NAMES
#-----------------------------------------------------------------
# GDS = Geriatric Depression Scale
# CES-D = The Centre for Epidemiological Studies Depression Scale (loneliness only item used)
# GAI = Geriatric Anxiety Inventory
# PSWQ = The Penn State Worry Questionnaire
# SF12 = The Short Form 12 version 1 Mental Health Subscale
#-----------------------------------------------------------------


# F-stats from the interaction between time and group for mean
# It was uncertain whether we could use these estimates. Therefore, we didn't use
# the reported F-statistics. 
F_stat_Wuthrich <- 
  tibble(
    
    year = c(13, 21, 21, 13, 21, 13, 13, 13),
    
    outcome = c(
      "Primary problem", 
      "Anxiety", 
      "Depression", 
      "GDS", 
      "Loneliness (CES-D 14)", 
      "GAI", 
      "PSWQ", 
      "SF12"),
    
    df1 = 1,
    df2 = c(
      
      47.95,  # Primary problem severity (2013, p. 783)
      49.603, # Anxiety disorder severity (2021, p. 444)
      45.451, # Depressive disorder severity [Mood] (2021, p. 444)
      50.88,  # GDS (2013, p. 783)
      40.609, # Loneliness (2021, p. 444)
      55.65,  # GAI (2013, p. 783)
      54.95,  # PSWQ (2013, p. 783)
      51.39   # SF15 (2013, p. 783)
      
      ),
    F_val = c(
      
      17.36, 
      17.858,
      30.884,
      8.86,
      7.070,
      7.4,
      0.57,
      qf(.988, df1 = 1, df2 = df2[8], lower.tail = FALSE)
      
    ),
    
    pval_reported = c(
      
      pf(F_val[1], df1 = 1, df2 = df2[1], lower.tail = FALSE),
      pf(F_val[2], df1 = 1, df2 = df2[2], lower.tail = FALSE),
      pf(F_val[3], df1 = 1, df2 = df2[3], lower.tail = FALSE),
      .004,
      .011,
      .009,
      .452,
      .988
      
    )
  
)

F_stat_Wuthrich
## # A tibble: 8 × 6
##    year outcome                 df1   df2      F_val pval_reported
##   <dbl> <chr>                 <dbl> <dbl>      <dbl>         <dbl>
## 1    13 Primary problem           1 47.95 17.36        0.0001283  
## 2    21 Anxiety                   1 49.60 17.86        0.0001018  
## 3    21 Depression                1 45.45 30.88        0.000001372
## 4    13 GDS                       1 50.88  8.86        0.004      
## 5    21 Loneliness (CES-D 14)     1 40.61  7.07        0.011      
## 6    13 GAI                       1 55.65  7.4         0.009      
## 7    13 PSWQ                      1 54.95  0.57        0.452      
## 8    13 SF12                      1 51.39  0.0002284   0.988
## emm = estimated marginal means

Wuthrich2013_2021_smd <- 
  tibble(
    
    outcome = rep(
      c("Primary problem", 
        "Anxiety", 
        "Depression", 
        "GDS", 
        "Loneliness (CES-D 14)",
        "GAI",
        "PSWQ",
        "SF12"
        ), 
      each = 2),
    
    group = rep(c("CBT", "Waitlist"), n_distinct(outcome)),
    
    N_start = rep(c(27, 35), n_distinct(outcome)), # From Table 2, 2013, p. 783
    
    N = rep(c(27, 35), n_distinct(outcome)), # From Table 2, 2013, p. 783
    
    b_emm_pre = c(
      
      6.33, 5.81,    # ADIS primary disorder severity (from Table 3, 2013, p. 784)
      5.2, 4.58,     # ADIS mean anxiety disorder severity (from Table 2, 2021, p. 445)
      5.48, 4.98,    # ADIS depressive disorder severity (from Table 2, 2021, p. 445)
      18.14, 16.35,  # Geriatric Depression Scale (from Table 3, 2013, p. 784)
      1.59, 1.36,    # Loneliness (CES-D item 14) (from Table 2, 2021, p. 445)
      11.59, 9.48,   # Geriatric Anxiety Inventory (from Table 3, 2013, p. 784)
      53.62, 49.27,  # Penn State Worry Questionnaire (from Table 3, 2013, p. 784) 
      37.09, 38.45   # Short Form-12 (Mental) (from Table 3, 2013, p. 784)
      
    ),
    
    b_emm_post = c(
      
      3.46, 5.15,   # ADIS primary disorder severity (from Table 3, 2013, p. 784)
      2.68, 4.01,   # ADIS mean anxiety disorder severity (from Table 2, 2021, p. 445)
      2.02, 4.25,   # ADIS depressive disorder severity (from Table 2, 2021, p. 445)
      9.21, 14.38,  # Geriatric Depression Scale (from Table 3, 2013, p. 784)
      .593, 1.24,   # Loneliness (CES-D item 14) (from Table 2, 2021, p. 445)
      5.84, 8.33,   # Geriatric Anxiety Inventory (from Table 3, 2013, p. 784)
      46.54, 47.24, # Penn State Worry Questionnaire (from Table 3, 2013, p. 784)
      47.79, 43.56  # Short Form-12 (Mental) (from Table 3, 2013, p. 784)
      
    ),
    
    se_emm_post = c(
      
      0.29, 0.24,
      0.307, .227,
      .319, .260,
      1.44, 1.23,
      0.169, 0.194,
      1.20, 1.04,
      2.80, 2.41,
      2.26, 1.97
      
    ),
    
    sd_pre = c(
      
      0.653, 1.16,  # ADIS primary disorder severity (from Table 2, 2013, p. 783)
      0.74, 1.07,   # ADIS mean anxiety disorder severity (from Table 1, 2021, p. 442)
      1.05, 1.27,   # ADIS depressive disorder severity (from Table 1, 2021, p. 442)
      6.18, 5.99,   # Geriatric Depression Scale (from Table 2, 2013, p. 783)
      0.82, 0.97,   # Loneliness (CES-D item 14)  (from Table 1, 2021, p. 442)
      4.72, 5.43,   # Geriatric Anxiety Inventory (from Table 2, 2013, p. 783)
      12.39, 13.06, # Penn State Worry Questionnaire (from Table 2, 2013, p. 783)
      9.22, 6.69    # Short Form-12 (Mental) (from Table 2, 2013, p. 783)
      
    ),
    
    # Number of controlled covariates
    q = rep(c(2, 4, 4, 2, 4, rep(2, 3)), each = 2)
    
  )

Wuthrich2013_2021_smd
## # A tibble: 16 × 9
##    outcome               group    N_start     N b_emm_pre b_emm_post se_emm_post
##    <chr>                 <chr>      <dbl> <dbl>     <dbl>      <dbl>       <dbl>
##  1 Primary problem       CBT           27    27      6.33      3.46        0.29 
##  2 Primary problem       Waitlist      35    35      5.81      5.15        0.24 
##  3 Anxiety               CBT           27    27      5.2       2.68        0.307
##  4 Anxiety               Waitlist      35    35      4.58      4.01        0.227
##  5 Depression            CBT           27    27      5.48      2.02        0.319
##  6 Depression            Waitlist      35    35      4.98      4.25        0.26 
##  7 GDS                   CBT           27    27     18.14      9.21        1.44 
##  8 GDS                   Waitlist      35    35     16.35     14.38        1.23 
##  9 Loneliness (CES-D 14) CBT           27    27      1.59      0.593       0.169
## 10 Loneliness (CES-D 14) Waitlist      35    35      1.36      1.24        0.194
## 11 GAI                   CBT           27    27     11.59      5.84        1.2  
## 12 GAI                   Waitlist      35    35      9.48      8.33        1.04 
## 13 PSWQ                  CBT           27    27     53.62     46.54        2.8  
## 14 PSWQ                  Waitlist      35    35     49.27     47.24        2.41 
## 15 SF12                  CBT           27    27     37.09     47.79        2.26 
## 16 SF12                  Waitlist      35    35     38.45     43.56        1.97 
##    sd_pre     q
##     <dbl> <dbl>
##  1  0.653     2
##  2  1.16      2
##  3  0.74      4
##  4  1.07      4
##  5  1.05      4
##  6  1.27      4
##  7  6.18      2
##  8  5.99      2
##  9  0.82      4
## 10  0.97      4
## 11  4.72      2
## 12  5.43      2
## 13 12.39      2
## 14 13.06      2
## 15  9.22      2
## 16  6.69      2
Wuthrich2013_2021_wide <- 
  Wuthrich2013_2021_smd |>
  mutate(group = case_match(group, "CBT" ~ "t", "Waitlist" ~ "c")) |> 
  pivot_wider(
    names_from = group,
    values_from = N_start:sd_pre
  ) |> 
  rename(n_covariates = q)

#Wuthrich2013_2021_wide

Calculating effect sizes and conducting a small number of clusters correction, as suggested by WWC (2021).

Wuthrich2013_2021_es_smd <- 
  Wuthrich2013_2021_wide |> 
  mutate(
    
    effect_size = "SMD",
    sd_used = "Pooled pretest SD",
    
    study = "Wuthrich et al.",
    main_es_method = "Posttest emmeans",
    
    
    R2 = R2_imp,
    R2_method = "Imputed",
    
    N_total = N_t + N_c,
    
    df_ind = N_total,
    
    # Reverting outcomes so all outcomes has the same direction
    b_emm = if_else(unique(outcome) != "SF12", (b_emm_post_t - b_emm_post_c) * -1, b_emm_post_t - b_emm_post_c),
    sd_pool = sqrt(((N_t-1)*sd_pre_t^2 + (N_c-1)*sd_pre_c^2)/(N_t + N_c - 2)), 
    
    d_adj = b_emm/sd_pool,
    vd_adj = (1/N_t + 1/N_c) * (1-R2) + d_adj^2/(2*df_ind),
    Wd_adj = (1/N_t + 1/N_c) * (1-R2),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_adj= J * d_adj,
    vg_adj = (1/N_t + 1/N_c) * (1-R2) + g_adj^2/(2*df_ind),
    Wg_adj = (1/N_t + 1/N_c) * (1-R2),

    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # "in blocks of 8 participants." (2013, p. 781)
    avg_cl_size = 8, 
    avg_cl_type = "From study (2013, p. 781)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size), 
    omega = 1 - 3 / (4 * df_adj - 1), 
    
    gt_adj = omega * d_adj * gamma_sqrt, 
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_adj, 
      model = "emmeans", 
      cluster_adj = FALSE, 
      R2 = R2, 
      q = n_covariates, 
      add_name_to_vars = "_adj", 
      vars = -c("var_term1_adj")
      )
  
  ) |> 
  ungroup()

Calculating odds ratio effect sizes from Recovery rates reported in Wuthrich & Rapee (2013, p. 784)

# Odd ratio based on recovery rates reported in Wuthrich 2013 (p. 784)

Wuthrich2013_OR_dat <- 
  tibble(
    study = "Wuthrich et al.",
    outcome = "Recovery rates",
    effect_size = "OR",
    main_es_method = "Proportions",
    
    N_start_t = 27,
    N_start_c = 35,
    
    N_t = 20,
    N_c = 26,
    p_t = 0.53, 
    p_c = 0.11,
    
    # Average cluster size in treatment group
    # "in blocks of 8 participants." (2013, p. 781)
    avg_cl_size = 8, 
    avg_cl_type = "From study (2013, p. 781)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    # OR calculation and cluster bias adjustment
    VIVECampbell::OR_calc(
      p1 = p_t, p2 = p_c, n1 = N_t, n2 = N_c, 
      ICC = icc, avg_cl_size = avg_cl_size, n_cluster_arms = 1
    )
    
  )

Wuthrich2013_OR_dat
## # A tibble: 1 × 19
##   study           outcome        effect_size main_es_method N_start_t N_start_c
##   <chr>           <chr>          <chr>       <chr>              <dbl>     <dbl>
## 1 Wuthrich et al. Recovery rates OR          Proportions           27        35
##     N_t   N_c   p_t   p_c avg_cl_size avg_cl_type                 icc icc_type
##   <dbl> <dbl> <dbl> <dbl>       <dbl> <chr>                     <dbl> <chr>   
## 1    20    26  0.53  0.11           8 From study (2013, p. 781)   0.1 Imputed 
##      OR ln_OR vln_OR    DE vln_OR_C
##   <dbl> <dbl>  <dbl> <dbl>    <dbl>
## 1 9.124 2.211 0.5936 1.352   0.8026
# Amalgamating the SMD and OR results
Wuthrich2013_2021_est <- 
  bind_rows(Wuthrich2013_2021_es_smd, Wuthrich2013_OR_dat) |> 
  mutate(
    study = "Wuthrich et al. 2013/2021",
    vary_id = outcome,
    analysis_plan = case_when(
           outcome =="Primary problem" ~ "All mental health outcomes/Anxiety", 
           outcome == "Anxiety" ~ "All mental health outcomes/Anxiety", 
           outcome == "Depression" ~ "All mental health outcomes/Depression", 
           outcome == "GDS" ~ "All mental health outcomes/Depression", 
           outcome == "Loneliness (CES-D 14)"~"Loneliness" ,
           outcome == "GAI"~"All mental health outcomes/Anxiety" ,
           outcome == "PSWQ"~"Unused outcomes",
           outcome == "SF12"~"Wellbeing and Quality of Life",
           outcome == "Recovery rates" ~ "Unused outcomes"  # Couldn't figure this one out (Jakob)
         )
  )


Wuthrich2013_2021_est
## # A tibble: 9 × 56
##   outcome               n_covariates N_start_t N_start_c   N_t   N_c b_emm_pre_t
##   <chr>                        <dbl>     <dbl>     <dbl> <dbl> <dbl>       <dbl>
## 1 Primary problem                  2        27        35    27    35        6.33
## 2 Anxiety                          4        27        35    27    35        5.2 
## 3 Depression                       4        27        35    27    35        5.48
## 4 GDS                              2        27        35    27    35       18.14
## 5 Loneliness (CES-D 14)            4        27        35    27    35        1.59
## 6 GAI                              2        27        35    27    35       11.59
## 7 PSWQ                             2        27        35    27    35       53.62
## 8 SF12                             2        27        35    27    35       37.09
## 9 Recovery rates                  NA        27        35    20    26       NA   
##   b_emm_pre_c b_emm_post_t b_emm_post_c se_emm_post_t se_emm_post_c sd_pre_t
##         <dbl>        <dbl>        <dbl>         <dbl>         <dbl>    <dbl>
## 1        5.81        3.46          5.15         0.29          0.24     0.653
## 2        4.58        2.68          4.01         0.307         0.227    0.74 
## 3        4.98        2.02          4.25         0.319         0.26     1.05 
## 4       16.35        9.21         14.38         1.44          1.23     6.18 
## 5        1.36        0.593         1.24         0.169         0.194    0.82 
## 6        9.48        5.84          8.33         1.2           1.04     4.72 
## 7       49.27       46.54         47.24         2.8           2.41    12.39 
## 8       38.45       47.79         43.56         2.26          1.97     9.22 
## 9       NA          NA            NA           NA            NA       NA    
##   sd_pre_c effect_size sd_used           study                    
##      <dbl> <chr>       <chr>             <chr>                    
## 1     1.16 SMD         Pooled pretest SD Wuthrich et al. 2013/2021
## 2     1.07 SMD         Pooled pretest SD Wuthrich et al. 2013/2021
## 3     1.27 SMD         Pooled pretest SD Wuthrich et al. 2013/2021
## 4     5.99 SMD         Pooled pretest SD Wuthrich et al. 2013/2021
## 5     0.97 SMD         Pooled pretest SD Wuthrich et al. 2013/2021
## 6     5.43 SMD         Pooled pretest SD Wuthrich et al. 2013/2021
## 7    13.06 SMD         Pooled pretest SD Wuthrich et al. 2013/2021
## 8     6.69 SMD         Pooled pretest SD Wuthrich et al. 2013/2021
## 9    NA    OR          <NA>              Wuthrich et al. 2013/2021
##   main_es_method      R2 R2_method N_total df_ind   b_emm sd_pool    d_adj
##   <chr>            <dbl> <chr>       <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
## 1 Posttest emmeans     0 Imputed        62     62  1.69    0.9733  1.736  
## 2 Posttest emmeans     0 Imputed        62     62  1.33    0.9413  1.413  
## 3 Posttest emmeans     0 Imputed        62     62  2.23    1.180   1.890  
## 4 Posttest emmeans     0 Imputed        62     62  5.17    6.073   0.8513 
## 5 Posttest emmeans     0 Imputed        62     62  0.647   0.9080  0.7125 
## 6 Posttest emmeans     0 Imputed        62     62  2.49    5.134   0.4850 
## 7 Posttest emmeans     0 Imputed        62     62  0.7000 12.77    0.05480
## 8 Posttest emmeans     0 Imputed        62     62  4.230   7.887   0.5364 
## 9 Proportions         NA <NA>           NA     NA NA      NA      NA      
##     vd_adj   Wd_adj       J    g_adj   vg_adj   Wg_adj avg_cl_size
##      <dbl>    <dbl>   <dbl>    <dbl>    <dbl>    <dbl>       <dbl>
## 1  0.08992  0.06561  0.9879  1.715    0.08934  0.06561           8
## 2  0.08171  0.06561  0.9879  1.396    0.08132  0.06561           8
## 3  0.09442  0.06561  0.9879  1.867    0.09373  0.06561           8
## 4  0.07145  0.06561  0.9879  0.8410   0.07131  0.06561           8
## 5  0.06970  0.06561  0.9879  0.7039   0.06960  0.06561           8
## 6  0.06751  0.06561  0.9879  0.4791   0.06746  0.06561           8
## 7  0.06563  0.06561  0.9879  0.05413  0.06563  0.06561           8
## 8  0.06793  0.06561  0.9879  0.5298   0.06787  0.06561           8
## 9 NA       NA       NA      NA       NA       NA                 8
##   avg_cl_type                 icc icc_type gamma_sqrt df_adj   omega   gt_adj
##   <chr>                     <dbl> <chr>         <dbl>  <dbl>   <dbl>    <dbl>
## 1 From study (2013, p. 781)   0.1 Imputed       0.965  56.36  0.9871  1.654  
## 2 From study (2013, p. 781)   0.1 Imputed       0.965  54.36  0.9871  1.346  
## 3 From study (2013, p. 781)   0.1 Imputed       0.965  54.36  0.9871  1.801  
## 4 From study (2013, p. 781)   0.1 Imputed       0.965  56.36  0.9871  0.8109 
## 5 From study (2013, p. 781)   0.1 Imputed       0.965  54.36  0.9871  0.6787 
## 6 From study (2013, p. 781)   0.1 Imputed       0.965  56.36  0.9871  0.4620 
## 7 From study (2013, p. 781)   0.1 Imputed       0.965  56.36  0.9871  0.05220
## 8 From study (2013, p. 781)   0.1 Imputed       0.965  56.36  0.9871  0.5109 
## 9 From study (2013, p. 781)   0.1 Imputed      NA      NA    NA      NA      
##    vgt_adj  Wgt_adj   hg_adj  vhg_adj h_adj n_covariates_adj adj_fct_adj
##      <dbl>    <dbl>    <dbl>    <dbl> <dbl>            <dbl> <chr>      
## 1  0.1130   0.08870  0.7096   0.01774 58.36                2 eta        
## 2  0.1054   0.08870  0.5952   0.01840 58.36                4 eta        
## 3  0.1185   0.08870  0.7799   0.01840 58.36                4 eta        
## 4  0.09454  0.08870  0.3588   0.01774 58.36                2 eta        
## 5  0.09294  0.08870  0.3067   0.01840 58.36                4 eta        
## 6  0.09060  0.08870  0.2059   0.01774 58.36                2 eta        
## 7  0.08873  0.08870  0.02334  0.01774 58.36                2 eta        
## 8  0.09102  0.08870  0.2275   0.01774 58.36                2 eta        
## 9 NA       NA       NA       NA       NA                  NA <NA>       
##   adj_value_adj   p_t   p_c     OR  ln_OR  vln_OR     DE vln_OR_C
##           <dbl> <dbl> <dbl>  <dbl>  <dbl>   <dbl>  <dbl>    <dbl>
## 1         1.352 NA    NA    NA     NA     NA      NA      NA     
## 2         1.352 NA    NA    NA     NA     NA      NA      NA     
## 3         1.352 NA    NA    NA     NA     NA      NA      NA     
## 4         1.352 NA    NA    NA     NA     NA      NA      NA     
## 5         1.352 NA    NA    NA     NA     NA      NA      NA     
## 6         1.352 NA    NA    NA     NA     NA      NA      NA     
## 7         1.352 NA    NA    NA     NA     NA      NA      NA     
## 8         1.352 NA    NA    NA     NA     NA      NA      NA     
## 9        NA      0.53  0.11  9.124  2.211  0.5936  1.352   0.8026
##   vary_id               analysis_plan                        
##   <chr>                 <chr>                                
## 1 Primary problem       All mental health outcomes/Anxiety   
## 2 Anxiety               All mental health outcomes/Anxiety   
## 3 Depression            All mental health outcomes/Depression
## 4 GDS                   All mental health outcomes/Depression
## 5 Loneliness (CES-D 14) Loneliness                           
## 6 GAI                   All mental health outcomes/Anxiety   
## 7 PSWQ                  Unused outcomes                      
## 8 SF12                  Wellbeing and Quality of Life        
## 9 Recovery rates        Unused outcomes
#rm(Wuthrich2013_2021_smd_est, Wuthrich2013_OR_dat)

Amalgamating covariate and effect size data

# REMEMBER TO REMOVE EMPOWERMENT OUTCOME FROM BARBIC2009

#Remember to see working directory to "Group-based-interventions/ES calc"

cov_string <- if (isTRUE(getOption('knitr.in.progress')))  {
  "Descriptive coding scheme for group-based interventions (data).xlsx"
  } else {
    "ES calc/Descriptive coding scheme for group-based interventions (data).xlsx"
  }

covariates <- read_xlsx(cov_string, na = c(".", ""))

group_based_dat <- 
  bind_cols(
    covariates,
    dat
)

# To check the symmetry between R-file and Excel-extraction sheet
#D <- group_based_dat |> 
#  select(Author, vary_id, varifier)

#write_xlsx(group_based_dat, "Group-based interventions data.xlsx")
#saveRDS(group_based_dat, file = "Group-based interventions data.RDS")
#save(group_based_dat, file = "Group-based interventions data.RData")

Using extant data to estimate population standard deviations

To increase the generalization of the effect size estimates, we used extant data (i.e., all standard deviations from the control groups that has been measure on the same scale) to calculate population-based effect sizes. We only do so when more than four studies has reported the same outcomes on the same scale. This was recommended by Fitzgerald & Tipton (2024). We could calculate this standard deviation for the Global Assessment of Functioning Scale, Patient Health Questionnaire-9 (PHQ-9), and Beck Depression Inventory (BDI).  

Below, you find the function we used for these calculations–all of which can be found Fitzgerald & Tipton (2024).

# Function contain ANOVA estimation techniques from Fitzgerald & Tipton 2024
population_SMD <- 
  function(data){
  
  data |> 
  summarise(
    N = sum(N_total),
    N_0 = sum(N_c),
    J = n_distinct(study),
    sigma2_WA = sum((N_total-2)*sd_pool^2)/(N-2*J),
    m_post_mean_c = mean(m_post_c),
    SSB0 = sum(N_c*(m_post_c - m_post_mean_c)^2),
    MSB0 = SSB0/(J-1),
    n0_star = N_0 - (sum(N_c^2/N_0)/J-1),
    
    #Equation 12
    sigma2_B = (MSB0 - sigma2_WA)/n0_star,
    sigma2_TA = ((n0_star-1)/n0_star) * sigma2_WA + (MSB0/n0_star),
    sd_population = sqrt(sigma2_TA),
    n_star = (N_0 - sum(N_c^2/N_0))/(J-1),
    c1 = (n_star-1)*sigma2_WA/(n_star*(N-2*J)),
    c2 = (sigma2_WA + n_star * sigma2_B)/(n_star*(J-1)),
    v1 = N_0-2*J,
    v2 = J-1,
    # Eq. 29
    df_pop = (c1*v1 + c2*v2)^2/(c1^2*v1 + c2^2+v2),
    WaboveT = sigma2_WA/sigma2_TA
    
  )
    
}

GAF population measures

GAF_dat <- 
  dat |> 
  # Filtering GAF measures and remove Gonzalez and Prihoda since they don't
  # provide enough data to be used for this calculation
  filter(str_detect(outcome, "GAF") & !str_detect(study, "Gon")) |> 
  relocate(study) 

GAF_dat_restricted <- 
  GAF_dat |> 
  group_by(study) |> 
  filter(row_number() == 1) |> 
  ungroup()

ggplot(data.frame(x = c(0, 100)), aes(x)) + 
  mapply(function(mean, sd, col) {
    stat_function(fun = dnorm, args = list(mean = mean, sd = sd), linetype = "dashed", color = col)
  }, 
  # enter means, standard deviations and colors here
  mean = GAF_dat_restricted$m_post_c, 
  sd = GAF_dat_restricted$sd_post_c,
  col = "black"
  ) + 
  theme_bw() +
  labs(x = "GAF score", y = "Density")

GAF_res <- population_SMD(GAF_dat_restricted)
GAF_res
## # A tibble: 1 × 18
##       N   N_0     J sigma2_WA m_post_mean_c  SSB0  MSB0 n0_star sigma2_B
##   <dbl> <dbl> <int>     <dbl>         <dbl> <dbl> <dbl>   <dbl>    <dbl>
## 1   557   266     5     155.0         47.22 7833. 1958.   251.0    7.183
##   sigma2_TA sd_population n_star     c1    c2    v1    v2 df_pop WaboveT
##       <dbl>         <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl>
## 1     162.2         12.74  46.55 0.2773 2.628   256     4  217.1  0.9557
#saveRDS(GAF_res, "ES calc/GAF_res.rds")

PHQ-9 population measures

PHQ_9_dat <- 
  group_based_dat |>
  filter(
    str_detect(authors, "Cano|Hilden|Himle|Lloyd|Volpe") & 
    str_detect(outcome, "9") & measure_type ==  "Post-intervention"
  )

# Removing follow-up effect size from Cano-Vindel
PHQ_9_dat <- PHQ_9_dat[-2,]

ggplot(data.frame(x = c(0, 35)), aes(x)) + 
  mapply(function(mean, sd, col) {
    stat_function(fun = dnorm, args = list(mean = mean, sd = sd), linetype = "dashed", color = col)
  }, 
  # enter means, standard deviations and colors here
  mean = PHQ_9_dat$m_post_c, 
  sd = PHQ_9_dat$sd_post_c,
  col = "black"
  ) + 
  theme_bw() +
  labs(x = "PHQ-9 score", y = "Density")

PHQ_9_res <- population_SMD(PHQ_9_dat)
PHQ_9_res
## # A tibble: 1 × 18
##       N   N_0     J sigma2_WA m_post_mean_c  SSB0  MSB0 n0_star sigma2_B
##   <dbl> <dbl> <int>     <dbl>         <dbl> <dbl> <dbl>   <dbl>    <dbl>
## 1  1230   605     5     36.15         10.97 1240. 310.1   511.2   0.5359
##   sigma2_TA sd_population n_star      c1     c2    v1    v2 df_pop WaboveT
##       <dbl>         <dbl>  <dbl>   <dbl>  <dbl> <dbl> <dbl>  <dbl>   <dbl>
## 1     36.68         6.056  32.80 0.02872 0.4094   595     4  75.29  0.9854
#saveRDS(PHQ_9_res, "ES calc/PHQ_9_res.rds")

BDI population measures

BDI_dat <- 
  group_based_dat |>
  filter(
    str_detect(authors, "Craigie|Hagen|Jacob|Michalak|Rabenstein|Sacks|Schäfer") &
    str_detect(outcome, "BDI")
  )

BDI_dat <- BDI_dat[-c(2, 5, 7, 10:13, 15),]

ggplot(data.frame(x = c(0, 60)), aes(x)) + 
  mapply(function(mean, sd, col) {
    stat_function(fun = dnorm, args = list(mean = mean, sd = sd), linetype = "dashed", color = col)
  }, 
  # enter means, standard deviations and colors here
  mean = BDI_dat$m_post_c, 
  sd = BDI_dat$sd_post_c,
  col = "black"
  ) + 
  theme_bw() +
  labs(x = "BDI_dat score", y = "Density")

BDI_res <- population_SMD(BDI_dat)
BDI_res
## # A tibble: 1 × 18
##       N   N_0     J sigma2_WA m_post_mean_c   SSB0  MSB0 n0_star sigma2_B
##   <dbl> <dbl> <int>     <dbl>         <dbl>  <dbl> <dbl>   <dbl>    <dbl>
## 1  1109   493     7     139.4         22.02 12512. 2085.   478.8    4.064
##   sigma2_TA sd_population n_star     c1    c2    v1    v2 df_pop WaboveT
##       <dbl>         <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl>
## 1     143.5         11.98  64.41 0.1253 1.038   479     6  300.7  0.9717
#saveRDS(BDI_res, "ES calc/BDI_res.rds")

References

Acarturk, C., Uygun, E., Ilkkursun, Z., Yurtbakan, T., Kurt, G., Adam-Troian, J., … Kiselev, N. (2022). Group problem management plus (PM+) to decrease psychological distress among Syrian refugees in Turkey: a pilot randomised controlled trial. BMC Psychiatry, 22(8), 1–11. https://doi.org/10.1186/s12888-021-03645-w
Arbuckle, R., Frye, M. A., Brecher, M., Paulsson, B., Rajagopalan, K., Palmer, S., & Degl’ Innocenti, A. (2009). The psychometric validation of the Sheehan Disability Scale (SDS) in patients with bipolar disorder. Psychiatry Research, 165(1), 163–174. https://doi.org/10.1016/j.psychres.2007.11.018
Bækkelund, H., Ulvenes, P., Boon-Langelaan, S., & Arnevik, E. A. (2022). Group treatment for complex dissociative disorders: a randomized clinical trial. BMC Psychiatry, 22(1), 338. https://doi.org/10.1186/s12888-022-03970-8
Barbic, S., Krupa, T., & Armstrong, I. (2009). A randomized controlled trial of the effectiveness of a modified recovery workbook program: preliminary findings. Psychiatric Services, 60(4), 491–497. https://doi.org/10.1176/ps.2009.60.4.491
Bond, G. R., Kim, S. J., Becker, D. R., Swanson, S. J., Drake, R. E., Krzos, I. M., … Frounfelker, R. L. (2015). A controlled trial of supported employment for people with severe mental illness and justice involvement. Psychiatric Services, 66(10), 1027–1034. https://doi.org/10.1176/appi.ps.201400510
Cano-Vindel, A., Munoz-Navarro, R., JA, M., Ruiz-Rodriguez, P., LA, M., & Gonzalez-Blanch, C. (2021). Transdiagnostic group cognitive behavioural therapy for emotional disorders in primary care: the results of the PsicAP randomized controlled trial. Psychological Medicine, 1–13. https://doi.org/10.1017/S0033291720005498
Craigie, M. A., & Nathan, P. (2009). A Nonrandomized Effectiveness Comparison of Broad-Spectrum Group CBT to Individual CBT for Depressed Outpatients in a Community Mental Health Setting. Behavior Therapy, 40(3), 302–314. https://doi.org/10.1016/j.beth.2008.08.002
Crawford, M. J., Killaspy, H., Barnes, T. R. E., Barrett, B., Byford, S., Clayton, K., … Johnson, T. (2012). Group art therapy as an adjunctive treatment for people with schizophrenia: multicentre pragmatic randomised trial. Bmj, 344.
Dalgaard, N. T., Flensborg Jensen, M. C., Bengtsen, E., Krassel, K. F., & Vembye, M. H. (2022). PROTOCOL: Group‐based community interventions to support the social reintegration of marginalised adults with mental illness. Campbell Systematic Reviews, 18(3), e1254. https://doi.org/10.1002/cl2.1254
Druss, B. G., Singh, M., Esenwein, S. A. von, Glick, G. E., Tapscott, S., Tucker, S. J., … Sterling, E. W. (2018). Peer-Led Self-Management of General Medical Conditions for Patients With Serious Mental Illnesses: A Randomized Trial. Psychiatric Services, 69(5), 529–535. https://doi.org/10.1176/appi.ps.201700352
Druss, B. G., Zhao, L., Esenwein, S. A. von, Bona, J. R., Fricks, L., Jenkins-Tucker, S., … Lorig, K. (2010). The Health and Recovery Peer (HARP) Program: A peer-led intervention to improve medical self-management for persons with serious mental illness. Schizophrenia Research, 118(1), 264–270. https://doi.org/10.1016/j.schres.2010.01.026
Dyck, D. G., Short, R. A., Hendryx, M. S., Norell, D., Myers, M., Patterson, T., … McFarlane, W. R. (2000). Management of Negative Symptoms Among Patients With Schizophrenia Attending Multiple-Family Groups. Psychiatric Services, 51(4), 513–519. https://doi.org/10.1176/appi.ps.51.4.513
Fitzgerald, K. G., & Tipton, E. (2024). Using Extant Data to Improve Estimation of the Standardized Mean Difference. Journal of Educational and Behavioral Statistics, 10769986241238478. https://doi.org/10.3102/10769986241238478
Gatz, M., Brown, V., Hennigan, K., Rechberger, E., O’Keefe, M., Rose, T., & Bjelajac, P. (2007). Effectiveness of an integrated, trauma‐informed approach to treating women with co‐occurring disorders and histories of trauma: The Los Angeles site experience. Journal of Community Psychology, 35(7), 863–878.
Gestel-Timmermans, H. van, Brouwers, E. P. M., Assen, M. A. L. M. van, & Nieuwenhuizen, C. van. (2012). Effects of a peer-run course on recovery from serious mental illness: a randomized controlled trial. Psychiatric Services, 63(1), 54–60. https://doi.org/10.1176/appi.ps.201000450
Gonzalez, J. M., & Prihoda, T. J. (2007). A Case Study of Psychodynamic Group Psychotherapy for Bipolar Disorder. American Journal of Psychotherapy, 61(4), 405–422. https://doi.org/10.1176/appi.psychotherapy.2007.61.4.405
Gordon, A., Davis, P. J., Patterson, S., Pepping, C. A., Scott, J. G., Salter, K., & Connell, M. (2018). A randomized waitlist control community study of Social Cognition and Interaction Training for people with schizophrenia. British Journal of Clinical Psychology, 57(1), 116–130. https://doi.org/10.1111/bjc.12161
Gutman, S. A., Barnett, S., Fischman, L., Halpern, J., Hester, G., Kerrisk, C., … Wang, H. (2019). Pilot Effectiveness of a Stress Management Program for Sheltered Homeless Adults With Mental Illness: A Two-Group Controlled Study. Occupational Therapy in Mental Health, 35(1), 59–71. https://doi.org/10.1080/0164212X.2018.1538845
Hagen, R., Nordahl, H. M., Kristiansen, L., & Morken, G. (2005). A Randomized Trial of Cognitive Group Therapy vs. Waiting List for Patients with Co-Morbid Psychiatric Disorders: Effect of Cognitive Group Therapy after Treatment and Six and Twelve Months Follow-Up. Behavioural and Cognitive Psychotherapy, 33(1), 33–44. https://doi.org/10.1017/S1352465804001754
Haslam, C., Cruwys, T., Chang, M. X.-L., Bentley, S. V., Haslam, S. A., Dingle, G. A., & Jetten, J. (2019). GROUPS 4 HEALTH reduces loneliness and social anxiety in adults with psychological distress: Findings from a randomized controlled trial. https://doi.org/10.1037/ccp0000427
Hedges, L. V., Tipton, E., Zejnullahi, R., & Diaz, K. G. (2023). Effect sizes in ANCOVA and difference-in-differences designs. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12296
Hilden, H. M., Rosenstrom, T., Karila, I., Elokorpi, A., Torpo, M., Arajarvi, R., & Isometsa, E. (2021). Effectiveness of brief schema group therapy for borderline personality disorder symptoms: a randomized pilot study. Nordic Journal of Psychiatry, 75(3), 176–185. https://doi.org/10.1080/08039488.2020.1826050
Himle, J., Bybee, D., Steinberger, E., Laviolette, W., Weaver, A., Vlnka, S., … Lisa, O. (2014). Work-related CBT versus vocational services as usual for unemployed persons with social anxiety disorder: A randomized controlled pilot trial. Behaviour Research & Therapy, 63, 169–176. https://doi.org/10.1016/j.brat.2014.10.005
Ilić, I., Šipetić-Grujičić, S., Grujičić, J., Živanović Mačužić, I., Kocić, S., & Ilić, M. (2019). Psychometric properties of the world health organization’s quality of life (WHOQOL-BREF) questionnaire in medical students. Medicina, 55(12), 772.
Izquierdo, G., Débora, Pérez, V., Luisa, M., Moreno, L., Raquel, … Juan, F. (2021). Training coping skills and coping with stress self-efficacy for successful daily functioning and improved clinical status in patients with psychosis: A randomized controlled pilot study. Science Progress, 1–22. https://doi.org/10.1177/00368504211056818
Jacob, G., Gabriel, S., Roepke, S., Stoffers, J., Lieb, K., & Hammers, C.-H. (2010). Group therapy module to enhance self-esteem in patients with borderline personality disorder: A pilot study. International Journal of Group Psychotherapy, 60(3), 373–387. https://doi.org/10.1521/ijgp.2010.60.3.373
James, W., Preston, N. J., Koh, G., Spencer, C., Kisely, S. R., & Castle, D. J. (2004). A group intervention which assists patients with dual diagnosis reduce their drug use: a randomized controlled trial. Psychological Medicine, 34, 983–990. https://doi.org/10.1017/s0033291703001648
Kanie, A., Kikuchi, A., Haga, D., Tanaka, Y., Ishida, A., Yorozuya, Y., … Nakagome, K. (2019). The Feasibility and Efficacy of Social Cognition and Interaction Training for Outpatients With Schizophrenia in Japan: A Multicenter Randomized Clinical Trial. FRONTIERS IN PSYCHIATRY, 10. https://doi.org/10.3389/fpsyt.2019.00589
Lim, J. E., Kwon, Y.-J., Jung, S.-Y., Park, K., Lee, W., Lee, S.-H., … Choi, K.-H. (2020). Benefits of social cognitive skills training within routine community mental health services: Evidence from a non-randomized parallel controlled study. Asian Journal of Psychiatry, 54, 102314. https://doi.org/10.1016/j.ajp.2020.102314
Lloyd-Evans, B., Frerichs, J., Stefanidou, T., Bone, J., Pinfold, V., Lewis, G., … Chipp, B. (2020). The Community Navigator Study: Results from a feasibility randomised controlled trial of a programme to reduce loneliness for people with complex anxiety or depression. Plos One, 15(5), e0233535. https://doi.org/10.1371/journal.pone.0233535
Madigan, K., Brennan, D., Lawlor, E., Turner, N., Kinsella, A., JJ, O., … Eadbhard, O. (2013). A multi-center, randomized controlled trial of a group psychological intervention for psychosis with comorbid cannabis dependence over the early course of illness. Schizophrenia Research, 143(1), 138–142. https://doi.org/10.1016/j.schres.2012.10.018
McCay, E., Beanlands, H., Leszcz, M., Goering, P., Seeman, M. V., Ryan, K., … Vishnevsky, T. (2006). A Group Intervention to Promote Healthy Self-Concepts and Guide Recovery in First Episode Schizophrenia: A Pilot Study. https://doi.org/10.2975/30.2006.105.111
McCay, E., Heather, B., Robert, Z., Roy, P., Leszcz, M., Landeen, J., … Chan, E. (2007). A randomised controlled trial of a group intervention to reduce engulfment and self-stigmatisation in first episode schizophrenia. Australian e-Journal for the Advancement of Mental Health, 6(3). https://doi.org/10.5172/jamh.6.3.212
Michalak, J., Schultze, M., Heidenreich, T., & Schramm, E. (2015). A randomized controlled trial on the efficacy of mindfulness-based cognitive therapy and a group version of cognitive behavioral analysis system of psychotherapy for chronically depressed patients. Journal of Consulting and Clinical Psychology, 83(5), 951. https://doi.org/10.1037/ccp0000042
Morley, K. C., Sitharthan, G., Haber, P. S., Tucker, P., & Sitharthan, T. (2014). The efficacy of an opportunistic cognitive behavioral intervention package (OCB) on substance use and comorbid suicide risk: A multisite randomized controlled trial. Journal of Consulting and Clinical Psychology, 82(1), 130. https://doi.org/10.1037/a0035310
Morton, J., Snowdon, S., Gopold, M., & Guymer, E. (2012). Acceptance and commitment therapy group treatment for symptoms of borderline personality disorder: a public sector pilot study. Cognitive and Behavioral Practice, 19(4), 527–544. https://doi.org/10.1016/j.cbpra.2012.03.005
Patterson, T. L., McKibbin, C., Taylor, M., Goldman, S., Davila-Fraga, W., Bucardo, J., & Jeste, D. V. (2003). Functional Adaptation Skills Training (FAST): A Pilot Psychosocial Intervention Study in Middle-Aged and Older Patients With Chronic Psychotic Disorders. The American Journal of Geriatric Psychiatry, 11(1), 17–23. https://doi.org/10.1097/00019442-200301000-00004
Popolo, R., MacBeth, A., Canfora, F., Rebecchi, D., Toselli, C., Salvatore, G., & Dimaggio, G. (2019). Metacognitive Interpersonal Therapy in group (MIT-G) for young adults with personality disorders: a pilot randomized controlled trial. Psychology and Psychotherapy, 92(3), 342–358. https://doi.org/10.1111/papt.12182
Rabenstein, R., Pintzinger, N., Knogler, V., Kirnbauer, V., Lenz, G., & Schosser, A. (2015). Effectiveness of a cognitive-behavioral rehabilitation day clinic program - A waiting list controlled trial. Verhaltenstherapie, 25, 192–200. https://doi.org/10.1159/000437449
Ravesteijn, H. van, Wittkampf, K., Lucassen, P., Lisdonk, E. van de, Hoogen, H. van den, Weert, H. van, … Speckens, A. (2009). Detecting somatoform disorders in primary care with the PHQ-15. The Annals of Family Medicine, 7(3), 232–238. https://doi.org/10.1370/afm.985
Rosenblum, A., Matusow, H., Fong, C., Vogel, H., Uttaro, T., Moore, T. L., & Magura, S. (2014). Efficacy of dual focus mutual aid for persons with mental illness and substance misuse. Drug and Alcohol Dependence, 135, 78–87. https://doi.org/10.1016/j.drugalcdep.2013.11.012
Rüsch, N., Staiger, T., Waldmann, T., Dekoj, M. C., Brosch, T., Gabriel, L., … Becker, T. (2019). Efficacy of a peer-led group program for unemployed people with mental health problems: Pilot randomized controlled trial. International Journal of Social Psychiatry, 65(4), 333–337. https://doi.org/10.1177/0020764019846171
Russinova, Z., Gidugu, V., Bloch, P., Restrepo-Toro, M., & Rogers, E. S. (2018). Empowering individuals with psychiatric disabilities to work: Results of a randomized trial. https://doi.org/10.1037/prj0000303
Sacks, S., McKendrick, K., Vazan, P., Sacks, J. Y., & Cleland, C. M. (2011). Modified therapeutic community aftercare for clients triply diagnosed with HIV/AIDS and co-occurring mental and substance use disorders. AIDS Care, 23(12), 1676–1686. https://doi.org/10.1080/09540121.2011.582075
Sajatovic, M., MA, D., SJ, G., MS, B., KA, C., RW, H., … JR, C. (2009). A comparison of the life goals program and treatment as usual for individuals with bipolar disorder. Psychiatric Services (Washington, D.C.), 60(9), 1182–1189. https://doi.org/10.1176/ps.2009.60.9.1182
Saloheimo, H. P., Markowitz, J., Saloheimo, T. H., Laitinen, J. J., Sundell, J., Huttunen, M. O., … O. Katila, H. (2016). Psychotherapy effectiveness for major depression: a randomized trial in a Finnish community. BMC Psychiatry, 16, 1–9. https://doi.org/10.1186/s12888-016-0838-1
Schäfer, I., Lotzin, A., Hiller, P., Sehner, S., Driessen, M., Hillemacher, T., … Grundmann, J. (2019). A multisite randomized controlled trial of Seeking Safety vs. Relapse Prevention Training for women with co-occurring posttraumatic stress disorder and substance use disorders. https://doi.org/10.1080/20008198.2019.1577092
Schrank, B., Brownell, T., Jakaite, Z., Larkin, C., Pesola, F., Riches, S., … Slade, M. (2016). Evaluation of a positive psychotherapy group intervention for people with psychosis: pilot randomised controlled trial. Epidemiology and Psychiatric Sciences, 25(3), 235–246. https://doi.org/10.1017/S2045796015000141
Smith, R., Wuthrich, V., Johnco, C., & Belcher, J. (2021). Effect of group cognitive behavioural therapy on loneliness in a community sample of older adults: A secondary analysis of a randomized controlled trial. Clinical Gerontologist, 44(4), 439–449.
Somers, J. M., Moniruzzaman, A., Patterson, M., Currie, L., Rezansoff, S. N., Palepu, A., & Fryer, K. (2017). A randomized trial examining housing first in congregate and scattered site formats. PloS One, 12(1), e0168745. https://doi.org/10.1371/journal.pone.0168745
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097. https://doi.org/10.1001/archinte.166.10.1092
Tjaden, C., CL, M., W, den H., Castelein, S., Delespaul, P., Keet, R., … Kroon, H. (2021). Effectiveness of Resource Groups for Improving Empowerment, Quality of Life, and Functioning of People With Severe Mental Illness A Randomized Clinical Trial. JAMA PSYCHIATRY, 78(12), 1309–1318. https://doi.org/10.1001/jamapsychiatry.2021.2880
Valiente, C., Espinosa, R., Contreras, A., Trucharte, A., Caballero, R., Peinado, V., … Perdigón, A. (2022). A multicomponent positive psychology group intervention for people with severe psychiatric conditions; a randomized clinical trial. Psychiatric Rehabilitation Journal, 45(2), 103–113. https://doi.org/10.1037/prj0000509
Volpe, U., Torre, F., De Santis, V., Perris, F., & Catapano, F. (2015). Reading group rehabilitation for patients with psychosis: A randomized controlled study. Clinical Psychology & Psychotherapy, 22(1), 15–21. https://doi.org/10.1002/cpp.1867
Wojtalik, J., Eack, S., & Keshavan, M. (2019). Confirmatory efficacy of cognitive enhancement therapy for early schizophrenia: Results from a multi-site randomized trial. 2019 Congress of the Schizophrenia International Research Society, SIRS 2019. Orlando, FL United States., 45(Supplement 2), S184. https://doi.org/10.1093/schbul/sbz021.235
Wuthrich, V. M., & Rapee, R. M. (2013). Randomised controlled trial of group cognitive behavioural therapy for comorbid anxiety and depression in older adults. Behaviour Research and Therapy, 51(12), 779–786. https://doi.org/10.1016/j.brat.2013.09.002
WWC. (2021). Supplement document for Appendix E and the What Works Clearinghouse procedures handbook, version 4.1. Retrieved from https://ies.ed.gov/ncee/wwc/Docs/referenceresources/WWC-41-Supplement-508_09212020.pdf
Zuithoff, N. P. A., Vergouwe, Y., King, M., Nazareth, I., Wezep, M. J. van, Moons, K. G. M., & Geerlings, M. I. (2010). The Patient Health Questionnaire-9 for detection of major depressive disorder in primary care: consequences of current thresholds in a crosssectional study. BMC Family Practice, 11(1), 98. https://doi.org/10.1186/1471-2296-11-98
---
title: Effect size calculation for 'Group-based community interventions to support
  the social reintegration of marginalised adults with mental illness'
date: "Last updated `r format(Sys.time(), '%Y.%m.%d')`"
output: 
  html_document:
    toc: true
    code_folding: show
    code_download: true
bibliography: Group-based interventions.bib
link-citations: yes
csl: apa.csl
fontsize: 11pt
spacing: single
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(message = FALSE)
knitr::opts_chunk$set(warning = FALSE)

options(pillar.sigfig = 4) # ensure tibble include 4 digits
options(tibble.width = Inf)
options(dplyr.print_min = 310)
options(scipen = 10)
options(dplyr.summarise.inform = FALSE) # Avoids summarize info from tidyverse
```

```{r, echo = FALSE}
gotop::use_gotop()
```

## **Introduction**

This document contains effect size calculation for the Campbell systematic review 'Group-based community interventions to support the social reintegration of marginalised adults with mental illness' [@Dalgaard2022a].

### R package used for effect size calculation

```{r}
# To retrieved the function used to cluster bias correct effect sizes
# devtools::install_github("MikkelVembye/VIVECampbell")

library(tidyverse)
#library(dplyr)
library(readxl)
library(writexl)
library(purrr)
library(VIVECampbell)
library(metafor)

```

### ICC values used for cluster adjustment

```{r}
ICC_005 <- 0.05
ICC_01 <- 0.1
ICC_02 <- 0.2

ppcor_imp <- 0.5
R2_imp <- 0

grp_size_imp <- 8
```

# Loading extant data used to calculated population-based effect sizes
```{r}
# Loading data used to calculate population-based effect size estimates
GAF_string <- if (isTRUE(getOption('knitr.in.progress'))) "GAF_res.rds" else "ES calc/GAF_res.rds"
GAF_pop_res <- readRDS(GAF_string )

PHQ_string <- if (isTRUE(getOption('knitr.in.progress'))) "PHQ_9_res.rds" else "ES calc/PHQ_9_res.rds"
PHQ_pop_res <- readRDS(PHQ_string)

BDI_string <- if (isTRUE(getOption('knitr.in.progress'))) "BDI_res.rds" else "ES calc/BDI_res.rds"
BDI_pop_res <- readRDS(BDI_string)

```

## **Studies in meta-analysis**

### **Acarturk et al. [-@Acarturk2022] (Quality-checked)**

To calculate pretest-adjusted effect sizes, we first estimate pre-post and pre-followup correlations from the reported results in "Treatment effect" section (p. 7)

```{r Acarturk_ppcor}

# _fu = followup

Acarturk_text_res <- 
  tibble(
    
    outcome = rep(c("HSCL-25", "PCL-5", "PSYCHLOPS"), each = 2),
    
    timing = rep(c("Posttest", "Followup"), 3),
    
    N = rep(c(41, 44, 19), c(2, 2, 2)), # Obtained from df(t)/2
    
    m_pre = rep(c(2.34, 1.77, 4.06), each = 2),
    sd_pre = rep(c(0.60, 0.86, 0.76), each = 2),
    
    m_post = c(
      2.15, 2.11, # HSCL-25
      1.43, 1.19, # PCL-5
      3.82, 3.45  # PSYCHLOPS
      
      ),
    
    sd_post = c(
      0.60, 0.59,
      0.79, 0.78,
      0.86, 1.32
      ),
    
    
    paired_t = c(
      2.43, 2.83,
      2.61, 4.45,
      1.01, 2.57
      )
    
  ) |> 
  mutate(
    
    ppcor = ((sd_pre^2*paired_t^2 + sd_post^2*paired_t^2) - (m_post - m_pre)^2 * N)/
      (2*sd_pre*sd_post*paired_t^2),
    
    .by = c(outcome, timing)
    
  ); Acarturk_text_res

```

Entering data from Table 2 (p. 7)

```{r Acarturk_data}

Acarturk2022 <- 
  tibble(
    
    outcome = rep(c("HSCL-25", "PCL-5", "PSYCHLOPS"), each = 4),
    
    timing = rep(c("Posttest", "Followup"), each = 2, n_distinct(outcome)),
    
    group = rep(c("gPM+", "Control"), n_distinct(outcome)*2),
    
    N = rep(c(24, 22), n_distinct(outcome)*2),
    
    m_pre = c(
        rep(c(2.37, 2.31), 2), # HSCL-25
        rep(c(1.84, 1.70), 2), # PCL-5
        rep(c(4.20, 3.92), 2)  # PSYCHLOPS
        
      ),
    
    sd_pre = c(
        
        rep(c(0.58, 0.64), 2),
        rep(c(0.88, 0.86), 2),
        rep(c(0.68, 0.84), 2)
      
      ),
    
    m_post = c(
      
      2.01, 2.28, 2.07, 2.14, 
      1.27, 1.59, 1.12, 1.26,
      3.82, 3.82, 3.67, 3.23
      
    ),
    
    sd_post = c(
      
      0.59, 0.58, 0.52, 0.43,
      0.70, 0.86, 0.85, 0.70,
      1.00, 0.63, 1.32, 1.63
      
    ),
    
    es_paper = rep(c(
      
      0.48, 0.12,
      0.40, 0.18,
      0, -0.30
      
      ),
      each = 2
    ),
    
    pval_paper = rep(c(
      
      0.109, 0.698,
      0.185, 0.553,
      0.996, 0.319
      
      ),
      each = 2
    )
    
    
    
  ); Acarturk2022


wide_format_Acarturk2022 <- 
  function(data, filter_value, trt_name, ctr_name){
  
  filter_func <- 
    function(data, filter_value, trt_name, ctr_name){
      
        
    dat <- data |> dplyr::filter(timing == filter_value)
    
    dat |> 
    dplyr::mutate(group = case_match(group, trt_name ~ "t", ctr_name ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    )
    
    
  }
  
  purrr::pmap_dfr(
    list(filter_value), ~ 
    filter_func(data = data, filter_value = .x,
                trt_name = trt_name, ctr_name = ctr_name)) |> 
    arrange(outcome)
  
  
}

Acarturk2022_wide <- 
  wide_format_Acarturk2022(
    data = Acarturk2022, 
    filter_value = c("Posttest", "Followup"),
    trt_name = "gPM+",
    ctr_name = "Control"
  ) |> 
  mutate(
    es_paper = es_paper_t,
    es_paper_type = "d effect size from a mixed-model analysis",
    pval_paper = pval_paper_t
  ) |> 
  select(-c(es_paper_t:pval_paper_c)) 


```

Effect size calculation and cluster bias adjustment

```{r}

eta_acarturk_sqrt <- VIVECampbell::eta_1armcluster(
  N_total = 46, Nc = 22, avg_grp_size = 10, ICC = ICC_01,
  sqrt = TRUE
)

Acarturk2022_es <- 
  Acarturk2022 |>
  mutate(
    ppcor = rep(Acarturk_text_res$ppcor, each = 2)
  ) |> 
  summarise(
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Acarturk et al. 2022",
    main_es_method = "Raw diff-in-diffs",
    
    ppcor = ppcor[1],  
    ppcor_method = "Calculated from study",
    
    N_t = N[1],
    N_c = N[2],
    
    N_total = N_t + N_c, 
    
    N_start_t = N_t,
    N_start_c = N_c,
    
    df_ind = N_total,
    
    n_covariates = 1,
    
    es_paper = es_paper[1],
    pval_paper = pval_paper[1],
    tval_paper = qnorm(pval_paper/2, lower.tail = FALSE),
    se_es_paper = es_paper/tval_paper,
    se_es_paper = if_else(se_es_paper == 0, sqrt(sum(1/N)) * eta_acarturk_sqrt, se_es_paper),
    var_es_paper = se_es_paper^2,
    
    sd_pool = sqrt( sum((N-1)*sd_post^2)/ (N_total-2) ),
    m_diff_post = (m_post[1] - m_post[2]) * -1,
    
    # Difference in differences measures
    m_diff_t = (m_post[1] - m_pre[1]) * -1,  
    m_diff_c = (m_post[2] - m_pre[2]) * -1,
    
    DD = m_diff_t - m_diff_c,
    
    d_post = m_diff_post/sd_pool, 
    vd_post = sum(1/N) + d_post^2/(2*df_ind),
    Wd_post = sum(1/N),
    
    d_DD = DD/sd_pool,
    vd_DD = 2*(1-ppcor) * sum(1/N) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * sum(1/N),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = sum(1/N) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * sum(1/N) + g_DD^2/(2*df_ind), 
    Wg_DD = Wd_DD,
    
    .by = c(outcome, timing) 
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # " eight to ten participants per group" (p. 1)
    avg_cl_size = 10, 
    avg_cl_type = "From study (p. 1)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    

    gt_post = g_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = vgt_post:Wgt_post
    )
    
) |> 
ungroup()

Acarturk2022_est <- 
  left_join(Acarturk2022_wide, Acarturk2022_es) |> 
  relocate(study) |> 
  mutate(
    vary_id = paste0(outcome, "/", timing),
    analysis_plan = case_when(
      outcome == "HSCL-25" ~ "All mental health outcomes",
      outcome == "PCL-5" ~ "All mental health outcomes",
      outcome == "PSYCHLOPS" ~ "Wellbeing and Quality of Life",
      TRUE ~ NA_character_  # Fallback in case of unexpected outcomes
    )
  )

Acarturk2022_est

```

```{r, echo = FALSE}
dat <- 
  Acarturk2022_est |> 
  rename(m_post = m_diff_post, mean_diff = DD)
```

### **Barbic et al. [-@Barbic2009] (Quality-checked)**

Entering data from Table 3 (p. 495).

```{r Barbic et al 2009}
Barbic2009 <- tibble(
  outcome = rep(c("Hope Index", "Empowerment Scale", "Quality of Life", "Recovery Assessment"), each = 2),
  group = rep(c("Recovery Workbook", "Control"), dplyr::n_distinct(outcome)),
  N = rep(c(16, 17), dplyr::n_distinct(outcome)),
  
  N_start = rep(c(16,17), 4),
  
  m_pre = c(
    37.13, 36.00, 
    55.93, 63.88, 
    20.34, 20.70, 
    163.75, 156.41),
  
   sd_pre = c(
    6.53, 5.36, 
    6.91, 6.91, 
    5.01, 4.13, 
    22.60, 14.22),
  
  m_post = c(
    38.93, 35.06, 
    14.93, 61.96, # The post-measurement seems flawed from therefore we exclude it from the analysis
    21.60, 22.27, 
    168.81, 149.11),
  
  
  sd_post = c(
    5.34, 6.21, 
    10.43, 7.33, 
    3.35, 4.91, 
    20.11, 22.09)
); Barbic2009

Barbic2009_wide <- 
  Barbic2009 |> 
    mutate(group = case_match(group, "Recovery Workbook" ~ "t", "Control" ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    ) |> 
  mutate(
# Based on the degrees of freedom value reported in Barbic et al. (2009),
# we cannot replicate the report F_values. Therefore, we do not use any F_values for 
# for the effect size correlation

  p_val_f = c(.05, .21, .96, .02), 
  df1 = 1, 
  df2 = 31,
  F_val = qf(p_val_f, df1 = df1, df2 = df2, lower.tail = FALSE)
  )
```

Effect size calculating, including cluster-bias correction

```{r}
Barbic2009_est <- 
  Barbic2009_wide |>
  mutate(
    analysis_plan = rep(
      c("Hope, Empowerment & Self-efficacy", "Wellbeing and Quality of Life", 
        "Hope, Empowerment & Self-efficacy"), 
      c(2, 1, 1))
  ) |> 
  rowwise() |> 
  mutate(
    
    F_val = if_else(outcome == "Empowerment Scale", 14.93, F_val), # Retrieved from (p. 495)
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Barbic et al. 2009",
    main_es_method = "Raw diff-in-diffs",
    
    ppcor = ppcor_imp,  
    ppcor_method = "Imputed",
    
  #  R2 = NA_real_,
  #  R2_calc_method = "Not relevant",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Calculate d post
    m_post = if_else(outcome != "Empowerment Scale", m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # Account for the fact that some outcomes are on different scales
    m_diff_t = if_else(outcome != "Empowerment Scale", m_post_t - m_pre_t, (m_post_t - m_pre_t) * -1),
    m_diff_c = if_else(outcome != "Empowerment Scale", m_post_c - m_pre_c, (m_post_c - m_pre_c) * -1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    # Average cluster size in treatment group
    # "12 weekly two-hour group sessions with seven to nine participants." (p. 493)
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 493)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    
    vary_id = outcome
    

  ) |> 
  ungroup() |> 
  relocate(N_total, .after = N_c); Barbic2009_est

```

```{r, echo=FALSE}
# Adding Barbic to data
dat <- 
  dat |> 
  bind_rows(Barbic2009_est) |> 
  relocate(study)
```

### **Bond et al. [-@Bond2015] (Quality-checked)**

Entering data from Table 3 and 4 (p. 1032). Recovery Assessment Scale Scores are retrieved from the text on page 1032 reported under Table 3. In our analysis, we do not distinguish between different types of work as done in the paper. We, therefore, amalgamate all work categories. We coded the control group as the treatment group because the control is the group-based treatment, which is of our main concern.

```{r}
# _t = Work choice

Bond2015_continious <- 
  tibble(
    outcome = c("Recovery Assessment Scale", "Days hospitalized"),
    
    N_t = 43,
    N_c = c(42, 41),
    
    N_start_t = 44,
    N_start_c = 43,
    
    m_post_t = c(4.14, 4.93),
    sd_post_t = c(0.49, 7.59),
    m_post_c = c(4.14, 10.44), 
    sd_post_c = c(0.57, 23.07),
    
    analysis_plan = c("Hope, Empowerment & Self-efficacy", "Psychiatric hospitalization"),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Bond et al. 2015",
    main_es_method = "Posttest es",
    
    ppcor_method = "Posttest only",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Calculate d post
    m_post = if_else(outcome != "Days hospitalized", m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post
  ) |> 
  rowwise() |> 
  mutate(
    
    # Cluster bias correction
    # Average cluster size in treatment group
    # "Classes were scheduled weekly at two conveniently located sites" (p. 1029)
    # Assuming two class in the Work Choice group is 44/2 = 22
    avg_cl_size = 22, 
    avg_cl_type = "Guessed from study",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    n_covariates = 0,
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = -c(var_term1_post, n_covariates_post)
      ),
  
    
    vary_id = outcome
    
  )

Bond2015_binary <- 
  tibble(
    outcome = c("Employment", "Risk of Psychiatric Hospitalization"),
    N_t = c(43, 42), # See note a in Table for sample size of Table 4 (i.e., 42, 40, respectively)
    N_c = c(42, 40),
    N_start_t = 44, 
    N_start_c = 43,
    
    A = c(13, 12), 
    B = c(30, 31),  
    C = c(20, 11),
    D = c(22, 30),
    
    analysis_plan = c("Employment", "Psychiatric hospitalization"),
    
    effect_size = "OR",
    sd_used = "Not relevant",
    
    study = "Bond et al. 2015",
    main_es_method = "Raw events",
    
    n_covariates = 0,
    
    N_total = N_t + N_c,
    
    p_t = A/N_t,
    p_c = C/N_c,
    
    RD = p_t - p_c,
    vRD = (A*B)/N_t^3 + (C*D)/N_c^3,
    
    # Average cluster size in treatment group
    # "Classes were scheduled weekly at two conveniently located sites" (p. 1029)
    # Assuming two class in the Work Choice group is 44/2 = 22
    avg_cl_size = 22, 
    avg_cl_type = "Guessed from study",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    # OR calculation and cluster bias adjustment as recommended by Cochrane
    VIVECampbell::OR_calc(
      A = A, B = B, C = C, D = D, 
      ICC = icc, avg_cl_size = avg_cl_size, n_cluster_arms = 1
    ),
    
    vary_id = outcome
    
)

Bond2015_est <- bind_rows(Bond2015_continious, Bond2015_binary)
Bond2015_est
```

```{r, echo=FALSE}

dat <- 
  dat |> 
  bind_rows(Bond2015_est)

```

### **Bækkelund et al. [-@Bækkelund2022] (Quality-checked)**

Entering data from Table 5 (p. 10)

```{r Bækkelund et al 2022}
Bækkelund2022 <- tibble(
  group = rep(c("Intervention", #Intervention group
                "Control"),     #Control group
              each = 1, 4),
  
  
  outcome = rep(c("GAF-S",     # Global Assesment of Function - Split version
                  "SCL-90 R"),  # Symptom Checklist 90 Revised
                each = 2, 2), 
  
  
  timing = rep(c("post", "6m"), each = 4), 
  
  N = rep(c(
    29, 30, 
    29, 30), 
    each = 1, 2), 

  N_start = N,
  
  m_pre = rep(c(
    41.7, 40.9,
    1.8, 1.9),
    each = 1,2), 
  
  
  sd_pre = rep(c(
    5.7, 8.2,
    0.7, 0.9), 
    each = 1,2), 
  
  m_post = c(
    # 6 months
    43.9, 44.2,
    1.7, 1.9,
    
    # 36 months
    48.4, 45.7,
    1.7, 1.9),
  
  sd_post = c(
    # 6 months
    6.3, 8.7,
    0.8, 0.9,
    
    # 36 months
    6.3, 4.2,
    0.9, 1.0),
  
  #_rm = repeated measure
  d_rm = c(
    0.4, 0.4, 
    0.1, .0,
    1.1, 0.7,
    0.1, .0 # Was reported as 0.4 but we can infer from the Table that this might have been a reporting mistake 
    
  ),
  
  # To calculated the pre-posttest correlation, we need the standard deviation of
  # the mean differences (sd_diff). As can be seen from page 6. We can obtain
  # sd_diff from the reported within-group effect sizes as follows
  sd_diff = abs(m_post - m_pre)/d_rm,
  
  # The group individual pre-posttest correlation can be obtained as follows.
  # Note that we are not able to obtained correlations from d_rm = 0. This only 
  # concerns SCL90 pre-posttest effect sizes for the control group. Hereto, 
  # 
  r = (sd_pre^2 + sd_post^2 - sd_diff^2)/(2*sd_pre*sd_post)
  
  
  ) |> 
  mutate(
    # Here we assume that the pre-post correlation is equal among groups
    r = if_else(is.na(r), mean(r, na.rm = TRUE), r), 
    
    .by = c(outcome, timing)

  ) |> 
  mutate(
    # To estiamte the average pre-posttest correlation
    z = 0.5 * log( (1+r)/(1-r) ),
    v = 1/(N-3),
    w = 1/v
    
  ); Bækkelund2022


# Making the tibble into wideformat in order to estimate effect sizes
Bækkelund2022_wide <-
  Bækkelund2022 |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  ) 


# Effect size calculating 
Bækkelund2022_est <-           
  Bækkelund2022_wide |>
  mutate(
    
    study = "Bækkelund et al. 2022",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    analysis_plan = case_when(
      str_detect(outcome, "GAF-S") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "SCL-90 R") ~ "All mental health outcomes"),
    
    main_es_method = "Raw diff-in-diffs",
    
    mean_z =  (w_t*z_t + w_c*z_c)/(w_t + w_c),  
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "Calculated from study results",
    
    m_diff_t = m_post_t - m_pre_t, 
    m_diff_c = m_post_c - m_pre_c,
    # SCL-90 R lower scores are beneficial why these is reverted
    mean_diff = if_else(outcome != "GAF-S", (m_diff_t - m_diff_c)*-1,
                        m_diff_t - m_diff_c),
    
    
    # SCL-90 R lower scores are beneficial why these is reverted
    m_post = if_else(outcome != "GAF-S", (m_post_t - m_post_c)*-1,
                     m_post_t - m_post_c),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    N_total = N_t + N_c,
    df_ind = N_total, 
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind), 
    Wd_post = (1/N_t + 1/N_c), 
    
    d_DD = (mean_diff/sd_pool), 
    vd_DD = (1/N_t + 1/N_c) * 2*(1-ppcor) + d_DD^2/(2*df_ind), 
    Wd_DD = (1/N_t + 1/N_c) * 2*(1-ppcor), 
    
    J = 1 - 3/(4*df_ind-1), 
    
    g_post = J * (m_post/sd_pool),
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = (1/N_t + 1/N_c),
  
    g_DD = J * (mean_diff/sd_pool), 
    vg_DD = (1/N_t + 1/N_c) * 2*(1-ppcor) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD,
    
    .by = outcome
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # "each group was led by two therapists and had nine participants." (p. 4)
    avg_cl_size = 9,
    avg_cl_type = "Obtained from article (p. 4)",
    
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1, 
    
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc, sqrt = TRUE
    ),
    
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * ((m_diff_t - m_diff_c)/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste(outcome, timing, sep = "/")
    
  ); Bækkelund2022_est

```

```{r, echo=FALSE}
# Adding Bækkelund to data
dat <- 
  dat |> 
  bind_rows(Bækkelund2022_est) |> 
  relocate(study)
```

### **Cano-Vindel et al. [-@Cano-Vindel2021] (Quality-checked)**

Data entered from Tables 2 and 3 (pp. 3342-45)

```{r}
Cano_vindel2021 <- tibble(
  analysis = rep(c("ITT", "Per-protocol"), each = 80),
  group = rep(c("TAU", "TAU-GCBT"), each = 1,80),
  
  outcome = rep(c("GAD-7", "PHQ-9", "PHQ15", "working_life_Function",
                  "Socal_life_Function", "Family_life_Function", "Physical_QoL",
                  "Psychological_QoL", "Social_QoL", "Environment_QoL"), 
                each = 8,2),
  
  timing = rep(c("Post", "3m", "6m", "12m"), each = 2,20),
  
  N = c(rep(c(534, 527), 40), # ITT
        rep(c(316, 315, # per-protocol post-treatment
              238, 273,# per-protocol 3 months
              204, 229,# per-protocol 6 months
              180, 208), 1,80) # per-protocol 12 months
  ),
  
  N_start = rep(c(534,527), each = 80),
  
  m_pre = c(
    rep(c(12.1, 12.5), each = 1,4),  # ITT GAD-7 Anxiety Baseline)
    rep(c(13.5, 13.7), each = 1,4),  # ITT PHQ9
    rep(c(14, 14.3), each = 1,4), # ITT PHQ15
    rep(c(3.5, 3.6), each = 1,4), # ITT working life
    rep(c(4.6, 4.7), each = 1,4), # ITT social life
    rep(c(4.6, 4.8), each = 1,4), # ITT family life
    rep(c(22.4, 22.1), each = 1,4), # ITT Physical
    rep(c(16.9, 16.9), each = 1,4), # ITT Psychological
    rep(c(9.1, 9.1), each = 1,4), # ITT Social
    rep(c(25.3, 25.7), each = 1,4),  # ITT Environment 
      
    rep(c(12.1, 12.5), each = 1,4), # per-protocol GAD-7 Anxiety Baseline)
    rep(c(13.5, 13.7), each = 1,4), # per-protocol GAD-7 PHQ9
    rep(c(14, 14.3), each = 1,4),   # per-protocol GAD-7 PHQ15
    rep(c(3.5, 3.6), each = 1,4),   # per-protocol GAD-7 working life
    rep(c(4.6, 4.7), each = 1,4),   # per-protocol GAD-7 social life
    rep(c(4.6, 4.8), each = 1,4),   # per-protocol GAD-7 family life
    rep(c(22.4, 22.1), each = 1,4), # per-protocol GAD-7 Physical
    rep(c(16.9, 16.9), each = 1,4), # per-protocol GAD-7 Psychological
    rep(c(9.1, 9.1), each = 1,4),   # per-protocol GAD-7 Social
    rep(c(25.3, 25.7), each = 1,4)  # per-protocol GAD-7 Environment 
  ), 
  
  sd_pre = c(
    rep(c(4.7, 4.6), each = 1,4),  # ITT GAD-7 Standard deviation
    rep(c(5.4, 5.3), each = 1,4), # ITT PHQ9
    rep(c(4.8, 4.9),each = 1,4), # ITT PHQ15
    rep(c(3.1, 3.2), each = 1,4), # ITT working life
    rep(c(3, 3), each = 1,4), # ITT social life
    rep(c(3.1, 3), each = 1,4), # ITT family life
    rep(c(4.3, 4.3), each = 1,4), # ITT Physical
    rep(c(3.8, 3.8), each = 1,4), # ITT Psychological
    rep(c(2.4, 2.4), each = 1,4), # ITT Social
    rep(c(4.5, 4.6), each = 1,4),  # ITT Environment 
    
    rep(c(4.7, 4.6), each = 1,4),  # per-protocol GAD-7 Standard deviation
    rep(c(5.4, 5.3), each = 1,4), # per-protocol PHQ9
    rep(c(4.8, 4.9),each = 1,4), # per-protocol PHQ15
    rep(c(3.1, 3.2), each = 1,4), # per-protocol working life
    rep(c(3, 3), each = 1,4), # per-protocol social life
    rep(c(3.1, 3), each = 1,4), # per-protocol family life
    rep(c(4.3, 4.3), each = 1,4), # per-protocol Physical
    rep(c(3.8, 3.8), each = 1,4), # per-protocol Psychological
    rep(c(2.4, 2.4), each = 1,4), # per-protocol Social
    rep(c(4.5, 4.6), each = 1,4)  # per-protocol Environment 
      
    
  ), 
  m_post = c(
    9.5, 6.8, # ITT GAD-7 Anxiety post-treatment
    8.7, 7.3, # ITT GAD-7 Anxiety 3 months
    8.6, 6.9, # ITT GAD-7 Anxiety 6 months
    8.3, 6.6,  # ITT GAD-7 Anxiety 12 months
    
    10.8, 8.0, # ITT PHQ9 Depression post treatment
    10.2, 8.4, # ITT PHQ9 Depression 3 months
    9.8, 7.9, # ITT PHQ9 Depression 6 months
    9.4, 7.8, # ITT PHQ9 Depression 12 months
    
    11.7, 9.9, # ITT PHQ-15 post-treatment
    11.4, 10.1,# ITT PHQ-15 3 months
    11.1, 9.8, # ITT PHQ-15 6 months
    10.7, 9.4, # ITT PHQ-15 12 months
    
    3.0, 2.6, # ITT working life post-treatment
    2.7, 2.4, # ITT Working life 3 months 
    2.7, 2.1, # ITT working life 6 months
    3.1, 2.4, # ITT working life 12 months
    
    
    4.1, 3.2, # ITT social life post-treatment
    3.5, 3.2, # ITT social life 3 months 
    3.4, 2.7, # ITT social life 6 months
    3.8, 2.9, # ITT social life 12 months
    
    3.9, 3.1, # ITT Family life post-treatment
    3.5, 3.1, # ITT Family life 3 months 
    3.6, 2.7, # ITT Family life 6 months
    3.8, 2.8, # ITT Family life  12 months
    
    23.2, 24.7, # ITT Physical post-treatment
    23.5, 24.2, # ITT Physical 3 months
    23.6, 24.3, # ITT Physical 6 months
    24.2, 26.4, # ITT Physical12 months
    
    17.7, 19.2, # ITT Psychological post-treatment
    18.1, 18.9, # ITT Psychological 3 months
    18.5, 19.1, # ITT Psychological 6 months
    18.7, 20.8, # ITT Psychological 12 months
    
    9.4, 9.8, # ITT Social post-treatment
    9.5, 9.7, # ITT Social 3 months
    9.5, 9.7, # ITT Social 6 months
    9.7, 11.1, # ITT Social 12 months
    
    25.7, 27.2, # ITT Environment post-treatment 
    26.1, 26.9, # ITT Environment 3 months
    26.5, 27.1, # ITT Environment 6 months
    27.5, 31.2,  # ITT Environment 12 months
    
    10.2, 6.0, # Per-protocol GAD-7 Anxiety post treatment
    8.9, 6.7, # Per-protocol GAD-7 Anxiety 3 months
    8.8, 6.2, # Per-protocol GAD-7 Anxiety  6 months
    8.7, 5.8, # Per-protocol GAD-7 Anxiety 12 months 
    
    11.5, 7.0,  # Per-protocol PHQ9 Depression post-treatment
    10.3, 7.8, # Per protocol PHQ9 Depression 3 months
    10.0, 7.3,  # Per-protocol PHQ9 Depression 6 months
    9.7, 7.1, # Per-protocol PHQ9 Depression 12 months
    
    12.1, 9.1, # Per-protocol PHQ-15 post treatment
    11.7, 9.5, # Per protocol PHQ-15 3 months
    11.5, 9.2, # Per-protocol PHQ-15 6 months
    11.7, 8.8, # Per-protocol PHQ-15 12 months
    
    3.1, 2.4, # Per-protocol working life post treatment
    2.6, 2.5, # Per protocol Working life 3 months
    2.8, 1.9, # Per-protocol working life 6 months
    3.3, 2.0, # Per-protocol working life 12 months
    
    
    4.1, 2.9, # Per-protocol social life post treatment
    3.4, 3.1, # Per protocol social life 3 months
    3.6, 2.6, # Per-protocol social life 6 months
    4.0, 2.6, # Per-protocol social life 12 months
    
    4.0, 2.8, # Per-protocol Family life post treatment
    3.5, 3.0, # Per protocol Family life 3 months
    3.6, 2.6, # Per-protocol Family life 6 months
    3.9, 2.5, # Per-protocol Family life 12 months
    
    
    22.7, 25.1,# Per-protocol Physical post treatment
    23.2, 24.4, # Per protocol Physical 3 months
    23.1, 24.7, # Per-protocol Physical 6 months 
    22.7, 25.6, # Per-protocol Physical 12 months
    
    17.4, 19.9, # Per-protocol Psychological post treatment
    18.0, 19.3, # Per protocol Psychological 3 months
    18.3, 19.3, # Per-protocol Psychological 6 months
    18.3, 20.2, # Per-protocol Psychological 12 months
    
    9.2, 10.0, # Per-protocol Social post treatment
    9.3, 9.8, # Per protocol Social 3 months
    9.6, 9.8, # Per-protocol Social 6 months
    9.3, 10.0, # Per-protocol Social 12 months
    
    25.5, 27.8, # Per-protocol Environment post treatment
    26.1, 27.5, # Per protocol Environment 3 months
    26.4, 27.7, # Per-protocol Environment 6 months
    26.6, 28.3) # Per-protocol Environment 12 months
    ,
  
  sd_post = c(
    5.4, 4.7, # ITT GAD-7 Anxiety post-treatment
    5.3, 5.0, # ITT GAD-7 Standard deviation 3 months
    5.4, 5.1, # ITT GAD-7 Standard deviation 6 months
    5.7, 5.4,  # ITT GAD-7 Standard deviation 12 months
    
    6.4, 5.7, # ITT PHQ9 Depression standard deviation post-treatment 
    6.4, 6.0, # ITT PHQ9 Depression standard deviation 3 months
    6.4, 6.1, # ITT PHQ9 Depression standard deviation 6 months
    6.3, 5.9, # ITT PHQ9 Depression standard deviation 12 months
    
    5.2, 5.4, # ITT PHQ-15 standard deviation post-treatment
    5.1, 5.3, # ITT PHQ-15 standard deviation 3 months
    5.3, 5.6, # ITT PHQ-15 standard deviation 6 months
    5.6, 5.6, # ITT PHQ-15 standard deviation 12 months
    
    3.1, 3.0, # ITT working life standard deviation post-treatment
    3.0, 3.0, # ITT working life standard deviation 3 months
    3.0, 2.9, # ITT working life standard deviation 6 months
    3.3, 3.2, # ITT working life standard deviation 12 months
    
    3.1, 3.0, # ITT social life standard deviation post-treatment
    3.1, 2.9, # ITT social life standard deviation 3 months
    3.2, 3.1, # ITT social life standard deviation 6 months
    3.4, 3.4, # ITT social life standard deviation 12 months
    
    3.1, 2.9, # ITT Family life standard deviation post-treatment
    3.1, 3.1, # ITT Family life standard deviation 3 months
    3.2, 3.1, # ITT Family life standard deviation 6 months
    3.3, 3.2, # ITT Family life standard deviation 12 months
    
    4.5, 4.6, # ITT Physical standard deviation post-treatment
    4.6, 4.8, # ITT Physical standard deviation 3 months
    4.5, 4.4, # ITT Physical standard deviation 6 months
    5.1, 5.3, # ITT Physical standard deviation 12 months
    
    3.9, 4.0, # ITT Psychological standard deviation post-treatment
    3.9, 4.2, # ITT Psychological standard deviation 3 months
    3.8, 3.9, # ITT Psychological standard deviation 6 months
    4.4, 4.6, # ITT Psychological standard deviation 12 months
    
    3.1, 3.3, # ITT Social standard deviation post-treatment
    2.3, 2.3, # ITT Social standard deviation 3 months
    2.5, 2.2, # ITT Social standard deviation 6 months
    2.2, 2.6, # ITT Social standard deviation 12 months
    
    5.3, 5.6, # ITT Environment standard deviation post-treatment
    4.9, 5.1, # ITT Environment standard deviation 3 months
    4.8, 4.8, # ITT Environment standard deviation 6 months
    5.0, 5.3, # ITT Environment standard deviation 12 months
    
    5.5, 4.3, # Per-protocol GAD-7 Anxiety standard deviation post-treatment
    5.4, 4.9, # Per-protocol GAD-7 Anxiety standard deviation 3 months
    5.7, 4.9, # Per-protocol GAD-7 Anxiety standard deviation 6 months
    5.8, 5.3, # Per-protocol GAD-7 Anxiety standard deviation 12 months
    
    6.6, 5.2, # Per-protocol PHQ9 Depression standard deviation post-treatment
    6.5, 6.0, # Per-protocol PHQ9 Depression standard deviation 3 months
    6.6, 6.1, # Per-protocol PHQ9 Depression standard deviation 6 months
    6.5, 6.2, # Per-protocol PHQ9 Depression standard deviation 12 months
    
    5.2, 5.3, # Per-protocol PHQ-15 standard deviation post-treatment
    5.0, 5.4, # Per-protocol PHQ-15 standard deviation 3 months
    5.3, 5.7, # Per-protocol PHQ-15 standard deviation 6 months
    5.6, 5.7, # Per-protocol PHQ-15 standard deviation 12 months
    
    3.1, 2.9, # Per-protocol working life standard deviation post-treatment
    3.0, 2.9, # Per-protocol working life standard deviation 3 months
    3.0, 2.7, # Per-protocol working life standard deviation 6 months
    3.3, 2.7, # Per-protocol working life standard deviation 12 months
    
    3.1, 2.8, # Per-protocol social life standard deviation post-treatment
    3.2, 2.9, # Per-protocol social life standard deviation 3 months
    3.2, 2.8, # Per-protocol social life standard deviation 6 months
    3.3, 3.1, # Per-protocol social life standard deviation 12 months
    
    3.1, 3.1, # Per-protocol Family life standard deviation post-treatment
    3.1, 3.0, # Per-protocol Family life standard deviation 3 months
    3.1, 2.7, # Per-protocol Family life standard deviation 6 months
    3.3, 2.8,  # Per-protocol Family life standard deviation 12 months
    
    4.6, 4.7, # Per-protocol Physical standard deviation post-treatment
    4.8, 4.9, # Per-protocol Physical standard deviation 3 months
    4.8, 4.9, # Per-protocol Physical standard deviation 6 months
    5.1, 5.3, # Per-protocol Physical standard deviation 12 months
    
    4.2, 4.0, # Per-protocol Psychological standard deviation post-treatment
    4.0, 4.2, # Per-protocol Psychological standard deviation 3 months
    4.2, 4.2, # Per-protocol Psychological standard deviation 6 months
    4.4, 4.6, # Per-protocol Psychological standard deviation 12 months
    
    2.4, 2.7, # Per-protocol Social standard deviation post-treatment
    2.2, 2.4, # Per-protocol Social standard deviation 3 months
    2.5, 2.2, # Per-protocol Social standard deviation 6 months
    2.2, 2.6, # Per-protocol Social standard deviation 12 months
    
    4.7, 4.8, # Per-protocol Environment standard deviation post-treatment
    4.8, 5.2, # Per-protocol Environment standard deviation 3 months
    4.8, 5.0, # Per-protocol Environment standard deviation 6 months
    5.0, 5.3) # Per-protocol Environment standard deviation 12 months
  
)
```

Since we could not backout any pre-posttests correlation estiamtes, we used test-retest reliability measures from the given scale literature [@Spitzer2006; @Zuithoff2010; @Ilic2019; @VanRavesteijn2009; @Arbuckle2009] as recommended by Hedges et al [-@Hedges2023_es_ancova].

```{r}

test_retest_cano2021 <- 
  tibble(
    # Repeat 8 times to make it fit the es data (see below)
    outcome = unique(Cano_vindel2021$outcome), 
    tr_reliability = c(
      0.83, # From Spitzer et al. 2006
      0.94, # From Zuithoff et al. 2010
      0.60, # From Ravesteijn et al. 2009
      
      0.63, # From Arbuckle et al. 2009
      0.70, # From Arbuckle et al. 2009
      0.61, # From Arbuckle et al. 2009
      
      0.665, # Ilíc et al. 2019
      0.724, # Ilíc et al. 2019
      0.725, # Ilíc et al. 2019
      0.60   # Ilíc et al. 2019
      )
    
  ); test_retest_cano2021 


```

Effect size calculation, including cluster bias correction

```{r}

# Turning data into wide format
params <- tibble(
  filter_val1 = rep(unique(Cano_vindel2021$analysis), each = 4),
  filter_val2 = rep(c("Post", paste0(c(3, 6, 12), "m")), 2)
)

wide_cano2021_func <- 
  function(filter_val1, filter_val2){
  
    Cano_vindel2021 |> 
    filter(analysis == filter_val1 & timing == filter_val2) |> 
    mutate(group = case_match(group, "TAU-GCBT" ~ "t", "TAU" ~ "c")) |>
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    )   
      
  }
 
Cano_vindel2021_est <- 
  pmap(params, wide_cano2021_func) |> 
  list_rbind() |> 
  left_join(test_retest_cano2021) |> 
  rename(ppcor = tr_reliability) |> 
  mutate(
    analysis_plan = case_when(
      outcome == "GAD-7" ~ "All mental health outcomes/Anxiety",
      outcome == "PHQ-9" ~ "All mental health outcomes/Depression",
      outcome == "PHQ15" ~ "All mental health outcomes",
      str_detect(outcome, "Funct") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "QoL") ~ "Wellbeing and Quality of Life",
      TRUE ~ NA_character_
    )
  ) |>
  relocate(analysis_plan) |> 
  rowwise() |> 
  mutate(
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Cano-Vindel et al. 2021",
    main_es_method = "Raw diff-in-diffs",
    
    ppcor_method = "Based on test-retest reliability estimates",
    
    N_total = N_t + N_c,
    df_ind = N_total,
     
    # Calculate d post
    m_post = if_else(str_detect(outcome, "QoL"), m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
   
    # Account for the fact that some outcomes are on different scales
    m_diff_t = if_else(str_detect(outcome, "QoL"), m_post_t - m_pre_t, (m_post_t - m_pre_t) * -1),
    m_diff_c = if_else(str_detect(outcome, "QoL"), m_post_c - m_pre_c, (m_post_c - m_pre_c) * -1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    # Average cluster size in treatment group
    # "held over a 12–14-week period in small groups (8–10 patients) in the primary care centre" (p. 3338)
    avg_cl_size = 9, 
    avg_cl_type = "From study (p. 3338)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),

    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    

    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "9"),
      omega * ((m_diff_t - m_diff_c)/PHQ_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*PHQ_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste(analysis, outcome, timing, sep = "/")
    
  ) |> 
  ungroup(); Cano_vindel2021_est 
  

```

```{r, echo=FALSE}
# Adding Cano-vindel to data
dat <- 
  dat |> 
  bind_rows(Cano_vindel2021_est) 
```

### **Craigie & Nathan [-@Craigie2009] (Quality-checked)**

Entering data from Table 2 and Treatment Format $\times$ Time interaction estimate from text results reported on pages 307-08

```{r}
Craigie2009 <- tibble(
  analysis = rep(c(
    "ITT",
    "Completers"), each = 6),
  
  outcome = rep(c(
  "BDI_II", 
  "BAI",
  "Q_LES_Q"), each = 2,2),
  
  group = rep(c(
    "Individual",
    "Group"), each = 1, 6), 
  
  N = c(rep(c(116, 240), 3),
        rep(c(77, 157), 3)
        
  ),
  
  N_start = rep(c(116, 240), 6),
  
  # pre-treatment means 
  m_pre = c( 
    # Intention to treat
    30.0, 31.3, # BDI-II
    21.1, 20.2, # BAI 
    43.2, 42.3, # Q-LES-Q
    # Completers (treatment-on-treated)
    30.8, 30.7,
    20.7, 19.6,
    46.4, 42.9
  ), 
  
  # pre-treatment standard deviation 
  sd_pre = c(
    # Intention to treat
    10.4, 11.2,
    12.2, 11.3,
    14.7, 14.1,
    # Completers (treatment-on-treated)
    10.3, 11.1,
    12.0, 11.0,
    13.5, 13.8
  ),
  
  # post-treatment means
  m_post = c(
    # Intention to treat
    16.3, 20.9,
    12.9, 15.6,
    52.3, 50.8,
    # Completers (treatment-on-treated)
    11.7, 17.7,
    9.2, 13.7,
    62.7, 55.3
  ),
  
  # post-treatment means 
  sd_post = c(
    # Intention to treat
    12.2, 13.6,
    11.2, 11.4,
    19.9, 17.9,
    # Completers (treatment-on-treated)
    9.9, 12.4,
    8.3, 10.7,
    14.7, 15.8
  ),
  
  F_within = c(
    129.79, 187.21,
    64.96, 56.00,
    42.27, 79.70,
    
    192.23, 200.75, 
    86.27, 61.57,
    62.45, 101.19
  ),
  
  # From Wilson (2016) Eq 31.
  r = ((sd_pre^2*F_within + sd_post^2*F_within) - 
        (m_post-m_pre)^2 * N)/ (2 * sd_pre*sd_post*F_within),
    
  # For pooling of correlation coefficients 
  z = 0.5 * log( (1+r)/(1-r) ),
  v = 1/(N-3),
  w = 1/v
  
) 
```

Effect size calculation

```{r}
# Calculating preposttest-correlations across groups
ppcors_Craigie2009 <- 
  Craigie2009 |> 
  summarise(
    mean_z = sum(w*z)/sum(w),
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "F_values used, but calcuable from study and used in the population-based effect sizes",
    .by = c(analysis, outcome)
  )

ppcors_Craigie2009  

Craigie2009_est <- 
  Craigie2009 |> 
  mutate(group = case_match(group, "Group" ~ "t", "Individual" ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    ) |> 
  left_join(ppcors_Craigie2009, by = join_by(analysis, outcome)) |> 
  mutate(
  
  analysis_plan = rep(c("All mental health outcomes/Depression", "All mental health outcomes/Anxiety", 
                        "Wellbeing and Quality of Life"), 2),  
  effect_size = "SMD",
  sd_used = "Pooled posttest SD",
    
  study = "Craigie & Nathan 2009",
  main_es_method = "Raw diff-in-diffs",
  
  #ppcor_calc_method = "Not relevant - F_values used",
  
  F_val = c(
    5.96, 9.84, 0.17, # (entered from p. 307 left side bottom)
    13.86, 16.93, 2.74 # (entered from p. 308 right side top)
  ),
    
  N_total = N_t + N_c,
  df_ind = N_total,
  
  # Calculate d post
  m_post = if_else(outcome == "Q_LES_Q", m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
  sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
  
  d_post = m_post/sd_pool, 
  vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
  Wd_post = (1/N_t + 1/N_c), 
  
  J = 1 - 3/(4*df_ind-1),
    
  g_post = J * d_post,
  vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
  Wg_post = Wd_post,
  
  # Account for the fact that some outcomes are on different scales
  m_diff_t = if_else(outcome == "Q_LES_Q", m_post_t - m_pre_t, (m_post_t - m_pre_t) * -1),
  m_diff_c = if_else(outcome == "Q_LES_Q", m_post_c - m_pre_c, (m_post_c - m_pre_c) * -1),
    
  d_DD = (m_diff_t - m_diff_c)/sd_pool,
  vd_DD = d_DD^2/F_val + d_DD^2/(2*df_ind),
  Wd_DD = d_DD^2/F_val,
  
  g_DD = J * d_DD, 
  vg_DD = g_DD^2/F_val + g_DD^2/(2*df_ind),
  Wg_DD = g_DD^2/F_val
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # "groups consisted of between 5 to 12 patients" p. 306
    avg_cl_size = 8.5, 
    avg_cl_type = "From study (p. 305)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = vgt_post:Wgt_post
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      F_val = F_val,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste0(analysis, "/", outcome)
    
  ) |> 
  ungroup(); Craigie2009_est


```

```{r, echo = FALSE}
dat <- 
  dat |> 
  bind_rows(Craigie2009_est)
```

### **Crawford et al. [-@Crawford2012] (Quality-checked)**

Entering data from Table 2 (p. 7)

```{r}

Crawford2012 <- 
  tibble(
    
    outcome = rep(
      c(
        "Global assessment of functioning (GAF)", "Positive and negative syndrome scale",
        # "Positive symptoms score", "Negative symptoms score", "General symptoms score", 
        "Social functioning", "Wellbeing" 
        # "Satisfaction with care", "Morisky score"
      ), 
      each = 6),
    group = rep(rep(c("Standard care", "Activity groups", "Art therapy"), 2), n_distinct(outcome)),
    timing = rep(rep(c(12, 24), each = n_distinct(group)), n_distinct(outcome)),
    
    N_start = rep(rep(c(137, 140, 140), 2), n_distinct(outcome)),
    N = rep(c(121, 121, 119, 117, 121, 117), n_distinct(outcome)),
    
    m_pre = c(
      rep(c(44.9, 45.0, 44.8), 2),
      rep(c(72.6, 75.3, 74.3), 2),
      #rep(c(17.3, 18.2, 18), 2),
      #rep(c(18.5, 18.7, 18.7), 2),
      #rep(c(36.8, 37.6, 37.6), 2),
      rep(c(8.1,  9., 8.6), 2),
      rep(c(64.5, 59.1, 58.3), 2)
      #rep(c(24.9, 23.8, 24.8), 2),
      #rep(c(1.2, 1.2, 1.0), 2)
    ), 
    
    sd_pre = c(
      rep(c(12.6, 12.7, 13.1), 2),
      rep(c(21.5, 22., 23.7), 2),
      #rep(c(5.6, 6.8, 6.9), 2),
      #rep(c(7.5, 7., 7.1), 2),
      #rep(c(11.3, 12.5, 12.5), 2),
      rep(c(4.7, 4.8, 4.2), 2),
      rep(c(20.6, 19.5, 21.1), 2)
      #rep(c(5.7, 6.2, 5.7), 2),
      #rep(c(1.3, 1.3, 1.2), 2)
    ),
    
    m_post = c(
      45.7, 45.5, 44.9, # GLobal assessment of functioning 12 month
      46.8, 46.4, 45.6, # GLobal assessment of functioning 24 month
      
      71.2, 69.6, 72.7, # Positive and negative syndrome scale 12 m
      68.1, 66.9, 69.2, # Positive and negative syndrome scale 24 m
      
      #16.7, 16.1, 17.3, # Positive symptoms score 12m
      #16.1, 15.6, 16.8, # Positive symptoms score 24m
      #
      #18.2, 17.3, 18.4, # Negative symptoms score 12m
      #17.2, 16.4, 16.9, # Negative symptoms score 24m
      #
      #36.3, 35.9, 37,   # General symptoms score 12m
      #34.9, 34.9, 35.3, # General symptoms score 24m
      
      8.5, 8.1, 8.3, # Social functioning 12m
      8.1, 8, 8.2,   # Social functioning 24m
      
      64.1, 63.6, 59.6, # Wellbeing 12m 
      68.1, 66.1, 65.1 # Wellbeing 24m 
      
      #24.3, 25., 23.6,  # Satisfaction with care 12m
      #24.2, 24.9, 23.1, # Satisfaction with care 24m
      
      #0.7, 0.6, 0.7, # Morisky score 12m
      #0.6, 0.5, 0.6  # Morisky score 24m
      
    ),
    
    sd_post = c(
      14.4, 14.1, 14.6, 
      12.8, 13.6, 13.1, 
      
      24.6, 23.2, 27.3,
      20.7, 23.3, 21.8,
      
      #6.3, 5.9, 7.6,
      #5.5, 6.4, 6.5,
      #
      #7.7, 7.2, 8,
      #7.3, 6.8, 7.1, 
      #
      #13, 12.7, 14,
      #11.3, 12.4, 11.4,
      
      4.9, 4.6, 5.,
      4.8, 4.5, 4.8,
      
      23.7, 23.2, 20.8,
      18.8, 18.4, 18.6
      
      #6.4, 5.2, 6.5,
      #5.3, 5., 5.9,
      
      #1.1, .9, 1.,
      #.9, .9, .9
      
    )
    
  )

Crawford2012_act_wide <- 
  Crawford2012 |> 
  filter(!str_detect(group, "Art")) |> 
  mutate(group = case_match(group, "Activity groups" ~ "t", "Standard care" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  ) |> 
  mutate(
    treatment = "Activity groups"
  ) |> 
  relocate(treatment)
  
Crawford2012_art_wide <- 
  Crawford2012 |> 
  filter(!str_detect(group, "Act")) |> 
  mutate(group = case_match(group, "Art therapy" ~ "t", "Standard care" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  ) |> 
  mutate(
    treatment = "Art therapy"
  ) |> 
  relocate(treatment)

Crawford2012_est <- 
  bind_rows(Crawford2012_act_wide, Crawford2012_art_wide) |> 
  arrange(outcome, timing) |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "Positive") ~ "All mental health outcomes/Symptoms of psychosis",
      str_detect(outcome, "function") ~ "Social functioning (degree of impairment)",
      outcome == "Wellbeing" ~ "Wellbeing and Quality of Life",
      TRUE ~ NA_character_
    ),
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Crawford et al. 2012",
    main_es_method = "Raw diff-in-diffs",
    
    ppcor = ppcor_imp,  
    ppcor_method = "Imputed",
    
    m_post = if_else(!str_detect(outcome, "Positive"), m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    m_diff_t = if_else(!str_detect(outcome, "Positive"), m_post_t - m_pre_t, (m_post_t - m_pre_t) * -1),
    m_diff_c = if_else(!str_detect(outcome, "Positive"), m_post_c - m_pre_c, (m_post_c - m_pre_c) * -1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # "Art therapy and activity groups had up to eight members 
    
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 1)",
    
    # Icc value
    icc = case_when(
      str_detect(outcome, "Global") & timing == 12 ~ 0.175,
      str_detect(outcome, "Global") & timing == 24 ~ 0.06,
      str_detect(outcome, "Positive") ~ 0.47,
      str_detect(outcome, "Social|Wellbeing") ~ ICC_01,
      TRUE ~ NA_real_
    ),
    
    icc_type = case_when(
      str_detect(outcome, "Global|Positive") ~ "From study (p. 3)",
      str_detect(outcome, "Social|Wellbeing") ~ "Imputed",
      TRUE ~ NA_character_
    ),
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
 
    # Cluster-adjusting g_post
    gt_post =  omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = vgt_post:Wgt_post
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * ((m_diff_t - m_diff_c)/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste(outcome, timing, treatment, sep = "/"),
  
    timing = as.character(timing)
    
  ) |> 
  ungroup(); Crawford2012_est

rm(Crawford2012_act_wide, Crawford2012_art_wide)

```

```{r, echo=FALSE}
# Adding Crawford to data
dat <- 
  dat |> 
  bind_rows(Crawford2012_est)
```

### **Druss et al. [-@Druss2010] (Quality-checked)**

Entering data from Table 2 (p. 268)

```{r}

Druss2010 <- tibble(
  
  outcome = rep(c(
    # "patient_activation", # unused outcomes
    # "Physical_activity",
    # "medication_adherence",
    "QoL_physical",
    "QoL_mental"
  ), each = 2),
  
  group = rep(c(
    "HARP",
    "TAU"
  ), 2),
  
  N = rep(c(
    41,39
  ), 2),
  
  N_start = rep(c(
    41,39
  ), 2),
  
  m_pre = c(
    #48.3, 47.6, # patient activation
    #150, 154, # Physical_activity
    #1.5, 1.5, # medication_adherence
    36.9, 37.0, # QoL_physical
    33.3, 33.9 # QoL_mental
  ),
  
  sd_pre = c(
    #11.5, 12.3,
    #236, 194,
    #1.2, 1.4,
    10.3, 12.5,
    9.13, 9.3
  ),
  
  m_post = c(
    #52.0, 44.9,
    #191, 152,
    #1.3, 1.6,
    42.9, 40,
    36.8, 37.0
  ),
  
  sd_post = c(
    #10.1, 9.6,
    #278, 249,
    #1.3, 1.4,
    14.2, 13.7,
    10.0, 11.8
  )
  
)

Druss2010_est <- 
  Druss2010 |> 
  mutate(group = case_match(group, "HARP" ~ "t", "TAU" ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
  ) |> 
  mutate(
    
    analysis_plan = "Wellbeing and Quality of Life",
    
    N_total = N_t + N_c,
    
    p_val_f = c(
      #0.03, 0.4, 0.22, 
      0.41, 0.96
      ),
    
    # Not used - seems flawed
    F_val = qf(p_val_f, df1 = 1, df2 = N_total - 2, lower.tail = FALSE),
    
    ppcor = ppcor_imp,  
    ppcor_method = "Imputed",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Druss et al. 2010",
    main_es_method = "Raw diff-in-diffs",
   
    df_ind = N_total,
    
    m_post = if_else(outcome != "medication_adherence", m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    m_diff_t = if_else(outcome != "medication_adherence", m_post_t - m_pre_t, (m_post_t - m_pre_t) * -1),
    m_diff_c = if_else(outcome != "medication_adherence", m_post_c - m_pre_c, (m_post_c - m_pre_c) * -1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # We don't know the average group size. Therefore, we impute this value
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    ppcor_method = "F value used",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),

    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    

    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Druss2010_est

```

```{r, echo=FALSE}
# Adding Druss 2010 to data
dat <- 
  dat |> 
  bind_rows(Druss2010_est) |> 
  relocate(study)
```

### **Druss et al. [-@Druss2018] (Quality-checked)**

Data from Table 2 (p. 532), containing means and SD for health related quality of life (PCS and MCS),and Table 3 (p. 533), containing means and SD for RAS

```{r Druss2018}
# Data from table 2 (p. 532), containing means and SD for health related quality of life 
# (PCS and MCS),and table 3 (p. 533), containing means and SD for RAS

Druss2018 <- tibble(
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,6)),
  
  timing = rep(c("3m", "6m"), each = 6,1),
  
  outcome = as.factor(rep(c("RAS", # Recovery Assessment Scale
                  # Both of the below measures is related to health related quality of life
                  # as measured by the Short-Form Health Survey (SF-36) (see p. 531)
                        "PCS", # The physical component summary
                        "MCS"), # The mental component summary
                        each= 2,2)),
  
  N = rep(c(198, 202), each = 1,6),
  
  N_start = rep(c(198, 202), each = 1,6),
 
 
 m_pre = rep(c(
   3.72, 3.66,   # RAS
   32.73, 32.74, # PCS
   32.05, 32.04) # MCS
 , each = 1,2)
 ,
  
  sd_pre = rep(c(
    0.62, 0.57,    # RAS
    10.92, 11.29,  # PCS
    11.79, 11.36), # MCS
    each = 1,2) 
 ,
  
  m_post = c(
    3.89, 3.70,   # RAS 3 months
    34.49, 33.89, # PCS 3 months
    34.49, 34.25, # MCS 3 months
    
    3.87, 3.74,   # RAS 6 months
    35.42, 34.25, # PCS 6 months
    36.64, 34.54  # MCS 6 months
  ),
  
  sd_post = c(
    0.55, 0.68,   # RAS 3 months
    11.15, 10.41, # PCS 3 months
    11.15, 11.97, # MCS 3 months
    
    0.61, 0.59,   # RAS 6 months
    11.02, 11.52, # PCS 6 months
    12.28, 11.82  # MCS 6 months
  )
) |> 
#arrange(outcome) |> 
relocate(outcome, .after = group); Druss2018

# Making the druss2018 tibble wide in order to estimate the effect sizes and
# further analysis. 

# Turning data into wide format
Druss2018_wide <- 
  Druss2018 |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )   

Druss2018_est <- 
  Druss2018_wide |> 
  # Based on the degrees of freedom value reported in Druss et al. 2018 table 2 and 3
  mutate(
     analysis_plan = case_when(
      str_detect(outcome, "RAS") ~ "Hope, Empowerment & Self-efficacy",
      str_detect(outcome, "PCS|MCS") ~ "Wellbeing and Quality of Life",
      .default = NA_character_
    ),
    
    study = "Druss et al. 2018",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    ppcor = ppcor_imp, # Imputed ppcor
    ppcor_method = "Imputed",
    
  ) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    m_post =  (m_post_t - m_post_c),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    m_diff_t = m_post_t - m_pre_t,
    m_diff_c = m_post_c - m_pre_c,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) ,
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD,
    
    
  ) |>  
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # Average cluster size in treatment group
    # Harp intevention had six to ten partcipants 
    
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 530)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    )
    
  ) |> 
  ungroup() |> 
  mutate(vary_id = paste0(outcome, "/", timing)
  ); Druss2018_est

```

```{r, echo=FALSE}
# Adding Druss 2018 to data
dat <- 
  dat |> 
  bind_rows(Druss2018_est) 
```

### **Dyck et al. [-@Dyck2000] (Quality-checked)**

Data from table 2 (p. 516) containing means and standard deviation

```{r Dyck et al 2000}
Dyck2000 <- tibble(
  group = as.factor(c("Multiple-family group", 
                          "Standard care")),
  outcome = "MSANS", #The modified Scale for the Assessment of Negative Symptoms
  N = 21,
  
  N_start = 21,
  
  m_pre = c(
    7.9, 8.7
  ),
  
  m_post = c(
#    7.4, 9.1, # 3 months
#    7.2, 8.9, # 6 months
#    7.2, 8.9, # 9 months
    7.2, 8.4 # 12 months, the real post-treatment measure because the treatment
             # ends after 12 months.
  ),
  
  
  sd_pre = c(
    3.1, 3.3 
  ),
  
  sd_post = c(
#    2.3, 3.2, # 3 months
#    2.1, 2.7, # 6 months
#    2.1, 3,   # 9 months
    2, 3.1    # 12 months, the real post-treatment measure because the treatment
              # ends after 12 months.
  )
)



# Making the dyck2000 tibble wide in order to estimate the effect sizes and
# further analysis. 
Dyck2000_wide <-
  Dyck2000 |> 
  mutate (group = case_match(
    group, "Multiple-family group"  ~ "t", "Standard care" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )

# Effect size calculating 
Dyck2000_est <-           
  Dyck2000_wide |>
  mutate(
    analysis_plan = "All mental health outcomes/Negative symptoms",
    
    F_val = 6.1,
  
    study = "Dyck et al. 2000",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
  # The outcome MSANS reverted because lower score is beneficial
    m_post =  (m_post_t - m_post_c)*-1,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
  
  # The outcome MSANS reverted because lower score is beneficial
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = d_DD^2/F_val + d_DD^2/(2*df_ind),
    Wd_DD = d_DD^2/F_val,
    
    g_DD = J * d_DD, 
    vg_DD = g_DD^2/F_val + g_DD^2/(2*df_ind),
    Wg_DD = g_DD^2/F_val
  
  ) |> 
  rowwise() |> 
  mutate(
  
  # Average cluster size in treatment group
  
  avg_cl_size = 7, 
  avg_cl_type = "From study (p. 516)",
  
  # Imputed icc value
  icc = ICC_01,
  icc_type = "Imputed",
  
  ppcor_method = "F value used",
  
  n_covariates = 1,
  
  # Find info about the function via ?VIVECampbell::gamma_1armcluster
  gamma_sqrt = VIVECampbell::gamma_1armcluster(
    N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
  ),
  
  # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
  df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
  omega = 1 - 3/(4*df_adj-1),
  
  
  # Cluster-adjusting g_post
  gt_post = omega * d_post * gamma_sqrt,
  VIVECampbell::vgt_smd_1armcluster(
    N_cl_grp = N_t, 
    N_ind_grp = N_c, 
    avg_grp_size = avg_cl_size, 
    ICC = icc, 
    g = gt_post, 
    model = "posttest",
    cluster_adj = FALSE,
    add_name_to_vars = "_post",
    vars = c(vgt_post, Wgt_post)
  ),
  
  gt_DD = omega * d_DD * gamma_sqrt,
  VIVECampbell::vgt_smd_1armcluster(
    N_cl_grp = N_t, 
    N_ind_grp = N_c, 
    avg_grp_size = avg_cl_size, 
    ICC = icc, 
    g = gt_DD, 
    model = "DiD",
    cluster_adj = FALSE,
    F_val = F_val,
    q = n_covariates,
    add_name_to_vars = "_DD",
    vars = -var_term1_DD)

  
) |> 
ungroup() |> 
mutate(vary_id = outcome); Dyck2000_est
```

```{r, echo=FALSE}
# Adding Dyck to data
dat <- 
  dat |> 
  bind_rows(Dyck2000_est) 
```

### **van Gestel-Timmermans et al. [-@VanGestel-Timmermans2012] (Quality-checked)**

Missing data analysis to support the RoB 2 D4 assessment

```{r}
# Testing missing 

Timmermans_sample_size_dat <-
  tibble(
    
    outcome = rep(c("Empoverment", "Hope", "Quality", "Self-e", "Loneliness"), each = 4),
    timing = rep(c("3 months", "6 months"), each = 2, n_distinct(outcome)),
    #measure = rep(c("post", "followup"), each = 2, n_distinct(outcome)),
    group = rep(c("Treatment", "Control"), 10),
    
    N_start = rep(rep(c(168, 165), 10)),
    
    N_actual = c(
      136, 117, 121, 99,
      132, 118, 120, 97,
      124, 114, 111, 97,
      134, 116, 121, 100,
      138, 122, 125, 102
    )
    
); Timmermans_sample_size_dat
```

Missingness analysis (used to conduct risk of bias assessement)

```{r}
Timmermans_missingness <- 
  Timmermans_sample_size_dat |> 
  summarise(
    N_start_total = sum(N_start),
    N_actual_total = sum(N_actual),
    
    #percent_missing_total = round(mean(percent_missing)),
    
    percent_missing_total = round((1 - (N_actual_total/N_start_total)) * 100),
    
    RoB_D3_assess = case_when(
      percent_missing_total >= 25 ~ "High",
      percent_missing_total < 25 ~ "Some concerns",
      TRUE ~ NA_character_
    ),
    
    .by = c(outcome, timing)
  )

Timmermans_missingness 
```

Data is retrieved from Tables 2 & 3 (p. 58)

```{r}

Timmermans2012 <- 
  Timmermans_sample_size_dat |> 
  rename(N =  N_actual) |> 
  mutate(
    
    #From Table 2 (p. 58)
    
    m_pre = c(
      rep(c(3.40, 3.37), 2), # Empowerment
      rep(c(2.78, 2.76), 2), # Hope
      rep(c(4.32, 4.23), 2), # Quality of life
      rep(c(4.38, 4.33), 2), # Self-efficacy beliefs
      rep(c(6.40, 6.87), 2)  # Loneliness
    ),
    
    sd_pre = c(
      rep(c(0.49, 0.51), 2), # Empowerment
      rep(c(0.47, 0.48), 2), # Hope
      rep(c(0.88, 1), 2),    # Quality of life
      rep(c(0.82, 0.89), 2), # Self-efficacy beliefs
      rep(c(3.56, 3.40), 2)  # Loneliness
    ),
    
    m_post = c(
      
      3.55, 3.38, 3.59, 3.40, # Empowerment
      2.91, 2.79, 2.97, 2.73, # Hope
      4.49, 4.36, 4.63, 4.39, # Quality of life
      4.65, 4.35, 4.71, 4.4,  # Self-efficacy beliefs
      5.89, 6.27, 3.87, 3.68  # Loneliness
      
    ),
    
    sd_post = c(
      
      0.48, 0.53, 0.50, 0.56,
      0.47, 0.53, 0.46, 0.48,
      0.96, 1.07, 0.97, 1.05,
      0.81, 0.97, 0.93, 0.88,
      3.61, 3.55, 3.87, 3.68
      
    ),
    
    es_paper = rep(
      c(
        0.27, 0.36, 
        0.25, 0.48,
        0.03, 0.15,
        0.27, 0.23, 
        -0.04, 0.15
      ),
      each = 2
    ),
    
    # From Table 3 (p. 58)
    
    b_adj = c(
      .12, .021, .13, .046,   #Empowerment
      .10, .038, .17, .004,   #Hope
      .064, .11, .11, .15,    #Quality of life
      .23, .064, .21, .14,    #Self-efficacy beliefs
      .066, -.65, -.34, -.49  #Loneliness
      
    ),
    
    pval_b_adj = c(
      .016, .55, .012, .24,  #Empowerment
      .039, .28, .001, .91,  #Hope
      .471, .09, .24, .025,  #Quality of life
      .010, .31, .026, .042, #Self-efficacy beliefs
      .85, .008, .35, .07    #Loneliness
      
      ),
    
    ci_l_b_adj = c(
      .022, -.05,  #Empowerment Treatment T1, Empowerment Control T1 
      .026, -.025, #Empowerment Treatment T2, Empowerment Control T2
      NA, -.030, 
      .026, -.025,
      -.11, -.016, 
      -.071, 0.020,
      .059, -.062, 
      .027, .006,
      -.60, -1.14, 
      -1.05, -1.02
    ),
    
    ci_u_b_adj = c(
      .22, .092, #Empowerment Treatment T1, Empowerment Control T1 
      .23, .12, #Empowerment Treatment T2, Empowerment Control T2 
      NA, .11, 
      .23, .12, 
      .23, .24, 
      .29, .28,
      .40, .19, 
      .39, .27,
      .74, -.16, 
      .37, .042
    ),
    
    tval_b_adj = qt(pval_b_adj/2, df = N-2, lower.tail = FALSE),
    se_b_adj = b_adj/tval_b_adj
    
  ); Timmermans2012 


wide_format_Timmermans2012 <- function(data, filter){
  
  filter_func <- function(data, filter){
    
    dat <- data |> dplyr::filter(timing != filter)
    
    dat |> 
    dplyr::mutate(group = case_match(group, "Treatment" ~ "t", "Control" ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N_start:dplyr::last_col()
    )
    
    
  }
  
  purrr::pmap_dfr(list(filter), ~ filter_func(data = data, filter = .x )) |> 
    arrange(outcome)
  
  
}

# Continue from here

Timmermans2012_wide <- 
wide_format_Timmermans2012(data = Timmermans2012, filter = c("6 months", "3 months"))

rm(wide_format_Timmermans2012)

```

Effect sizes calculation

```{r}

Timmermans2012_es <- 
  Timmermans2012 |>
  summarise(
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Timmermans et al. 2012",
    main_es_method = "Pretest adj. means from repeated-measure ANOVA",
    
    ppcor = .76,  
    ppcor_method = "From study (p. 57)",
    
    
    N_t = N[1],
    N_c = N[2],
    
    N_total = N_t + N_c, 
    
    df_ind = N_total,
    
    n_covariates = 1,
    
    sd_pool = sqrt( sum((N-1)*sd_post^2)/ (N_total-2) ),
    m_diff_post = m_post[1] - m_post[2],
    
    # Pretest adjusted means
    mean_b_adj = b_adj[1] - b_adj[2],
    
    # Difference in differences measures
    m_diff_t = m_post[1] - m_pre[1],  
    m_diff_c = m_post[2] - m_pre[2],
    
    DD = m_diff_t - m_diff_c,
    
    es_paper = es_paper[1],
    es_paper_type = "Cohens d",
    
    # Converting Loneliness measure since "Possible scores range from 0 to 11, 
    # with higher scores indicating more loneliness." (p. 58)
    d_post = if_else(unique(outcome) == "Loneliness", m_diff_post/sd_pool * -1, m_diff_post/sd_pool),
    vd_post = sum(1/N) + d_post^2/(2*df_ind),
    Wd_post = sum(1/N),
    
    d_adj = if_else(unique(outcome) == "Loneliness", mean_b_adj/sd_pool * -1, mean_b_adj/sd_pool),
    vd_adj = sum((se_b_adj/sd_pool)^2) + d_adj^2/(2*df_ind),
    Wd_adj = sum((se_b_adj/sd_pool)^2),
    
    
    d_DD = if_else(unique(outcome) == "Loneliness", DD/sd_pool * -1, DD/sd_pool),
    vd_DD = 2*(1-ppcor) * sum(1/N) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * sum(1/N),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = sum(1/N) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    g_adj = J * d_adj, 
    vg_adj = sum((se_b_adj/sd_pool)^2) + g_adj^2/(2*df_ind), 
    Wg_adj = Wd_adj,
    
    g_DD = J * d_DD,
    vg_DD = 2*(1-ppcor) * sum(1/N) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD,
    
    
    .by = c(outcome, timing) 
    
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # "was 7.0±2.1 (range of three to 12)" (p. 57)
    avg_cl_size = 7, 
    avg_cl_type = "From study (p. 57)",
    
    # Imputed icc value
    icc = 0.06,
    icc_type = "From study (p. 56)",
    
    n_covariates = 1,
  
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = vgt_post:Wgt_post
    ),
    
    gt_adj = omega * d_adj * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_adj, 
      model = "std_reg_coef",
      cluster_adj = TRUE,
      SE_std = sqrt(Wg_adj),
      add_name_to_vars = "_adj",
      vars = -var_term1_adj
    ),
    
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      )
    
  ) |> 
  ungroup()

# Timmermans2012_es

Timmermans2012_est <- 
  left_join(Timmermans2012_wide, Timmermans2012_es) |> 
  select(-c(es_paper_c, es_paper_t), -contains(c("ci_", "val_b"))) |> 
  mutate(vary_id = paste0(outcome, "/", timing),
         analysis_plan = case_when(
           outcome == "Empoverment"  ~  "Hope, Empowerment & Self-efficacy", 
           outcome == "Hope"  ~ "Hope, Empowerment & Self-efficacy", 
           outcome == "Quality"  ~ "Wellbeing and Quality of Life", 
           outcome == "Self-e"  ~ "All mental health outcomes", 
           outcome == "Loneliness"  ~ "Loneliness"
         )
      ) |> 
  rename(m_post = m_diff_post, mean_diff = DD)


```

```{r, echo=FALSE}

dat <- 
  dat |> 
  bind_rows(Timmermans2012_est)

```

### **Gatz et al. [-@Gatz2007] (Quality-checked)**

Entering data from Table 2 (p. 872)

```{r Gatz et al 2007}
Gatz2007 <- tibble(
  outcome = rep(c("ASI alcohol", "ASI drug", "GSI", "PSS", "Coping skills"), each = 2),
  group = rep(c("Intervention", "Comparison"), dplyr::n_distinct(outcome)),
  N = c(136, 177, 135, 176, 136, 177, 136, 177, 134, 173),
  N_start = rep(c(187, 215), each = 5),
  
  m_pre = c(
    .18, .27, 
    .19, .25, 
    1.07, 1.09, 
    20.43, 19.05,
    52.61, 54.26),
  
  m_post = c(
    .06, .13, 
    .04, .08, 
    .8, .86, 
    13.78, 15.11,
    56.32, 52.96),
  
  sd_pre = c(
    .29, .35, 
    .15, .14, 
    .68, .69, 
    10.22, 11.84,
    17.83, 17.51),
  
  sd_post = c(
    .17, .23, 
    .07, .11, 
    .72, .77, 
    11.1, 12.91,
    20.15, 20.09)
)


```

Effect size calculation and cluster bias adjustment

```{r}

Gatz2007_est <- 
  Gatz2007 |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Comparison" ~ "c")) |> 
  tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    ) |> 
  mutate(
    F_val = c(0.53, 1.23, 0.34, 3.99, 4.12), # Table 2 (p. 872)
    
    analysis_plan = c(
      "Alcohol and drug abuse/misuse", "Alcohol and drug abuse/misuse",
      "All mental health outcomes", "All mental health outcomes", "Hope, Empowerment & Self-efficacy"
    ),
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Gatz et al. 2012",
    main_es_method = "Raw diff-in-diffs",
    
    m_post = m_post_t - m_post_c,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # Account for the fact that some outcomes are on different scales
    m_diff_t = m_post_t - m_pre_t, 
    m_diff_c = m_post_c - m_pre_c, 
      
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = d_DD^2/F_val + d_DD^2/(2*df_ind),
    Wd_DD = d_DD^2/F_val,
    
    g_DD = J * d_DD, 
    vg_DD = g_DD^2/F_val + g_DD^2/(2*df_ind),
    Wg_DD = g_DD^2/F_val
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # Imputed since group sizes were not reported
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    ppcor_method = "F value used",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),

    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      F_val = F_val,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Gatz2007_est

```

```{r, echo=FALSE}
# Adding Gatz to data
dat <- 
  dat |> 
  bind_rows(Gatz2007_est) 
```

### **Gonzalez and Prihoda [-@Gonzalez2007] (Quality-checked)**

Data extracted from Table 3 (p. 413)

```{r Gonzalez and Prihoda 2007}
Gonzalez2007_est <- 
  tibble(
    study = "Gonzalez & Prihoda 2007",
    
    analysis_plan = c(
      "Social functioning (degree of impairment)",
      "All mental health outcomes"
    ),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD (assumed)",
    
    main_es_method = "Repeated-measure ANOVA with emm",
    ppcor_method = "F value used",
    
    outcome = c("GAF", "CGI"),
    N_start_t = 11, 
    N_start_c = 11,
    # Actual samples sizes
    N_t = 8, # Three patients terminated the group early (p. 411)
    N_c = 9, # Two of the control subjects were also lost two follow-up (p. 412)
    
    N_total = N_t + N_c,
    
    es_paper = c(0.20, 0.66),
    es_paper_type = "Cohen d (assumed)",
    
    b_emm_post_t = c(72.20, 1.75),
    b_emm_post_c = c(69.30, 2.61),
    
    se_emm_post_t = c(4.11, 0.37),
    se_emm_post_c = c(4.11, 0.37),
    
    mean_b_emm = if_else(outcome == "GAF", (b_emm_post_t - b_emm_post_c), (b_emm_post_t - b_emm_post_c) * -1),
    
    sd_total = abs(mean_b_emm)/es_paper,
    # Using E.14 from WWC Handbook (ver. 5). We imputed the covariate-adjusted mean difference to obtain this estimate
    sd_pool = sqrt( ((N_total-1)/(N_total-2)) * sd_total^2 - ((N_t*N_c)/(N_total*(N_total-2))) * mean_b_emm^2),
    
    pval_paper = c(
      0.65, 
      0.15 # Is reported as <0.05 and must be estimated from the corresponding F_val
      ),
    F_val = c(0.22, 2.34),
    
    #pval_test = pf(F_val, df1 = 1, df2 = 11, lower.tail = FALSE)

    df_ind = N_total,
    
    d_adj = es_paper,
    vd_adj = d_adj^2/F_val + d_adj^2/(2*N_total),
    Wd_adj = d_adj^2/F_val,
    
    J = 1 - 3/(4*df_ind-1),
    
    g_adj = J * d_adj,
    vg_adj = g_adj^2/F_val + g_adj^2/(2*N_total),
    Wg_adj = g_adj^2/F_val
  
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # he group, with 7 to 11 active members
    avg_cl_size = 7, 
    avg_cl_type = "From study (p. 408)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
  
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_adj = omega * d_adj * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_adj, 
      model = "emmeans",
      cluster_adj = FALSE,
      F_val = F_val,
      add_name_to_vars = "_adj",
      vars = -var_term1_adj
    ),
    
    gt_adj_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * (mean_b_emm/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_adj_pop = if_else(
      str_detect(outcome, "GAF"),
      # We did not use the F value for variance estimation here
      GAF_pop_res$WaboveT * (1/N_t + 1/N_c) * adj_value_adj + gt_adj_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_adj_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (1/N_t + 1/N_c) * adj_value_adj,
      NA_real_
      ),
    
    vary_id = outcome
    
  ); Gonzalez2007_est
```

```{r, echo=FALSE}

dat <- 
  dat |> 
  bind_rows(Gonzalez2007_est)

```

### **Gordon et al. [-@Gordon2018] (Quality-checked)**

Entering data from Table 2 (p. 124)

```{r}

Gordon2018 <- tibble(
  
  outcome = as.factor(rep(c(
   "QLS_total",
   #"QLS_interpersonal",
   #"QLS_ instrumental",
   #"BLERT",
   "LSP",
   #"SCSQ-ToM",
   "SSPA",
   "DASS-21"
   #"AIHQ-Hostility-bias"
   ), 
   each = 2)),
  
  group = rep(c("SCIT", "Control"), n_distinct(outcome)),

  N = rep(c(21, 15), n_distinct(outcome)),
  N_start = rep(c(21, 15), 4),
 
 m_pre = c(
   50.24, 45.73, # QLS total
   #15.86, 13.40, # QLS interpersonal subscale 
   #6.19, 6.00,   # QLS instrumental subscale
   #14.43, 14.40, # BLERT
   13.38, 14.53, # LSP
   #7.33, 7.07,   # SCSQ-ToM
   65.19, 59.07, # SSPA
   43.33, 53.33  # DASS-21
    # 8.43, 8.53   # AIHQ-Hostility-bias
  ),  
 
 sd_pre = c(
   15.83, 13.85, 
   #7.26, 7.18, 
   #5.94, 6.21, 
   #3.62, 3.15, 
   7.44, 6.12, 
   #1.62, 1.79, 
   12.03, 10.57, 
   26.20, 32.73 
   #3.80, 4.24
 ),
 
 m_post = c(
   56.30, 47.67, 
   #17.40, 14.47, 
   #6.95, 6.53, 
   #15.90, 15.93, 
   10.29, 13.60,
   #7.50, 7.07, 
   71.25, 63.57, 
   41.10, 49.13
   #6.80, 8.87
 ),
 
 sd_post = c(
   16.34, 13.19, 
   #8.33, 6.57, 
   #5.48, 6.63, 
   #3.62, 2.76, 
   4.59, 6.10, 
   #1.53,1.40, 
   8.22, 7.89, 
   25.96, 31.40 
   #1.73, 2.29
 )
 
); Gordon2018

```

Effect size calculation and cluster bias adjustment

```{r}

Gordon2018_est <- 
  Gordon2018 |> 
  mutate(group = case_match(group, "SCIT" ~ "t", "Control" ~ "c")) |> 
    tidyr::pivot_wider(
      names_from = group,
      names_glue = "{.value}_{group}",
      values_from = N:last_col()
    ) |> 
  mutate(
    F_val = c(
      2.503, #0.019, 
      0.905, #0.632, 
      0.039, 0.573 # 2.99
      ), 
    
    analysis_plan = case_when(
      outcome == "DASS-21" ~ "All mental health outcomes",
      str_detect(outcome, "SSPA|LSP") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "QLS") ~ "Wellbeing and Quality of Life",
      TRUE ~ NA_character_
    ),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Gordon et al. 2018",
    main_es_method = "Raw diff-in-diffs",
    
    
    ppcor = ppcor_imp,  
    ppcor_method = "Imputed - F-vals seemed flawed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Calculate d post
    m_post = m_post_t - m_post_c,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # Account for the fact that some outcomes are on different scales
    m_diff_t = m_post_t - m_pre_t, 
    m_diff_c = m_post_c - m_pre_c, 
      
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # Imputed since group sizes were not reported
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),

    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Gordon2018_est

```

```{r, echo=FALSE}
# Adding Gordon to data
dat <- 
  dat |> 
  bind_rows(Gordon2018_est)
```

### **Gutman et al. [-@Gutman2019] (Quality-checked)**

Entering raw data from Table 2 (p. 67)

```{r}

Gutman2019_raw <- 
  tibble(
    outcome = rep(c("PSS", "WHOQOL-BREF"), each = 20),
    
    group = rep(c("t", "c"), each = 10, 2),
    
    N = 10,
    
    pre_test = c(
      25, 37, 34, 33, 19, 25, 36, 34, 32, 35, # PSS intervention
      25, 30, 31, 34, 26, 21, 32, 34, 27, 35, # PSS Control
      121, 114, 84, 89, 82, 83, 86, 70, 119, 93, # WHOQOL intervention
      97, 113, 98, 93, 86, 93, 98, 57, 91, 101 # WHOQOL control
    ),
    
    post_test = c(
      21, 32, 30, 29, 15, 23, 32, 27, 28, 30, 
      27, 30, 33, 41, 30, 27, 32, 36, 33, 36,
      125, 118, 95, 89, 87, 91, 93, 77, 125, 107,
      100, 112, 95, 90, 88, 85, 100, 43, 74, 102
    )
    
  ) |> 
  mutate(
    across(pre_test:post_test, ~ (.x - mean(.x))/sd(.x), .names = "{.col}_std"),
    
    .by = outcome
    
  )

```

Effect size calculation

```{r}

# Calculate raw means first and combine with _est object
Gutman2019 <- 
  Gutman2019_raw |> 
  summarise(
    
    N = length(post_test),
    
    m_pre = mean(pre_test),
    sd_pre = sd(pre_test),
    m_post = mean(post_test),
    sd_post = sd(post_test),
    
    r = cor(pre_test, post_test),
    
    .by = c(outcome, group)
  ) |> 
  pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  ) |> 
  mutate(
    N_total = N_t + N_c,
    N_start_t = 21, # p. 120
    N_start_c = 15,
    df_ind = N_total
  )

Gutman2019_cor <- 
  Gutman2019_raw |> 
  summarise(
    
    ppcor = cor(pre_test, post_test, method = "pearson"),
    ppcor_method = "From study - raw data",
    
    .by = outcome
  )

Gutman2019 <- left_join(Gutman2019, Gutman2019_cor, by = join_by(outcome))

Gutman2019_est <-
  Gutman2019_raw |> 
  summarise(
    
    d_reg = summary(lm(post_test_std ~ group + pre_test_std, data = pick(everything())))$coefficients[2],
    var_term1_reg = summary(lm(post_test_std ~ group + pre_test_std, data = pick(everything())))$coefficients[5]^2,
    
    .by = outcome
  
  ) |> 
  mutate(
    
    d_reg = if_else(outcome == "PSS", d_reg * -1, d_reg)
    
  ) |> 
  left_join(Gutman2019, by = join_by(outcome)) |> 
  mutate(
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    study = "Gutman et al. 2019",
    main_es_method = "Standardized regression from reported raw data",
    
    ppcor = Gutman2019_cor$ppcor,
    ppcor_method = Gutman2019_cor$ppcor_method,
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Calculate d post
    m_post = if_else(outcome == "WHOQOL-BREF", m_post_t - m_post_c, (m_post_t - m_post_c) * -1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c), 
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post
    
    
  ) |> 
  relocate(c(d_reg:var_term1_reg), .after = last_col()) |> 
  mutate(
    
  vd_reg = var_term1_reg + d_reg^2/(2*df_ind),
  Wd_reg = var_term1_reg,
  
  g_reg = J * d_reg, 
  vg_reg = var_term1_reg + g_reg^2/(2*df_ind),
  Wg_reg = var_term1_reg,
  
  ) |>
  rowwise() |> 
  mutate(
  avg_cl_size = 10, # Assuming one cluster of 10 only 
  avg_cl_type = "From study",
    
  # Imputed icc value
  icc = ICC_01,
  icc_type = "Imputed",
  
  n_covariates = 1, 
  
  gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
 
  # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
  df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
  omega = 1 - 3/(4*df_adj-1),
    
  gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
      ),
  
  gt_reg = omega * d_reg * gamma_sqrt,
  VIVECampbell::vgt_smd_1armcluster(
    N_cl_grp = N_t,
    N_ind_grp = N_c,
    avg_grp_size = avg_cl_size,
    ICC = icc, 
    g = gt_reg, 
    model = "std_reg_coef",
    cluster_adj = FALSE,
    SE_std = var_term1_reg,
    add_name_to_vars = "_reg"
  ),
  
  vary_id = outcome,

  analysis_plan = case_when(
      str_detect(outcome, "PSS") ~ "All mental health outcomes",
      str_detect(outcome, "WHO") ~ "Wellbeing and Quality of Life"),
    
  ) |> 
  select(-var_term1_reg); Gutman2019_est


```

```{r, echo=FALSE}

dat <- 
  dat |> 
  bind_rows(Gutman2019_est)

```

### **Hagen et al. [-@Hagen2005] (Quality-checked)**

Entering data from Table 2 (p. 39) 

```{r Hagen et al 2018}
# Data from table 2, p. 39 
Hagen2005 <- tibble(
  
  outcome = rep(c(
    
  "SCL-90",
  "BAI",
  "BDI",
  "IIP-64-C"
  #"SAS-A",
  #"SAS-S",
  #"YSQ"
  ), 
  each = 2,1
 ),
 
 group = rep(c(
     
   "waiting-list", "CBGT"
   ),
   each = 1,4
 ),
 
 N = c(
     17, 15, 
     17, 15, 
     15, 15, 
     17, 14 
     # 16, 14, 
     # 16, 15, 
     # 16, 14
   ),
 
 N_start = rep(c(17,15), each = 1,4),
 
 m_pre = c(
     1.19, 1.15,
     20.29, 22.60,
     18.20, 15.40,
     1.35, 1.22
    # 73.06, 62.93,
    #  73.87, 70.80, 
    #  11.50, 7.21
 ),
 
  sd_pre = c(
      0.61, 0.61,
      10.08, 14.77,
      8.55, 9.86,
      0.72, 0.63
     # 20.23, 15.66,
     # 20.26, 19.16,
     # 14.15, 8.62
  ) ,
 
 m_post = c(
   1.15, 0.88, 
   22.94, 18.13, 
   18.35, 11.00, 
   1.28, 1.18 
  # 60.12, 58.57, 
  # 69.12, 60.14, 
  # 10.17, 6.63
 ),
 
 sd_post = c(
   0.47, 0.59,  
   13.46, 12.00,
   9.00, 8.36,
   0.68, 0.76
  # 14.17, 18.67,
  # 13.99, 22.52,
  # 13.41, 11.16
 ),

); Hagen2005


# Making the Hagen2006 tibble wide in order to estimate the effect sizes and
# further analysis. 

Hagen2005_wide <-
  Hagen2005 |> 
  mutate (group = case_match(
    group, "CBGT"  ~ "t", 
    "waiting-list" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )

# Effect size calculating 
Hagen2005_est <-           
  Hagen2005_wide |>
  mutate(
    
    analysis_plan = case_when(
      str_detect(outcome, "SCL-90") ~ "All mental health outcomes",
      str_detect(outcome, "BAI") ~ "All mental health outcomes/Anxiety",
      str_detect(outcome, "BDI") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "IIP-64-C") ~ "Social functioning (degree of impairment)",
      .default = NA_character_
    ),
    
    # ANCOVA results from table 3 (p. 39)
  #  F_val = c(
    #  8.46,
  #    2.23,
   #   5.51,
  #    0.18
  #  ),
    
  #  df1 = c(1),
  #  df2= c(
  #    28,
  #    28,
  #    27,
  #    27
  #  )
  
  ) |> 
  mutate(
    # Imputed ppcor
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    study = "Hagen et al. 2005",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # For all measures lower scores are beneficial why these are reverted
    m_post = (m_post_t - m_post_c)*-1, 
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # All outcomes are reverted because lower score is beneficial
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |>
  rowwise() |> 
   mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # Average cluster size in treatment group
    # Harp intevention had six to ten partcipants 
    
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 35)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD),
    
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
    )|> 
  ungroup() |> 
  mutate(vary_id = outcome); Hagen2005_est
```

```{r, echo=FALSE}
# Adding Hagen to data
dat <- 
  dat |> 
  bind_rows(Hagen2005_est) 
```

### **Haslam et al. [-@Haslam2019] (Quality-checked)**

Entering data from Table 2 (p. 794)

```{r Haslam et al 2019}
# Study only provides raw means and standard errors for pre-measurements and only estimated marginal 
# means with standard errors for post-measurements.

#NOT USED
Haslam2019 <- tibble(
  group = rep(c("Group 4 health",
                "TAU"), each = 1,5
  ),
  outcome =rep(c(
    "Loneliness",
    "Depression",
    "Social Anxiety",
    "General practitioner vists",
    "Multiple group memberships"
  ), each = 2,1),
  
  N = rep(c(66,54), each = 1,5), 
  
  # Table 1, p. 791
  m_pre = c(
    25.13, 25.00,
    20.82, 19.70,
    3.39, 3.53,
    1.41, 0.78,
    1.75, 1.84
  ),
  
  sd_pre = c(
    3.11, 2.78,
    9.96, 9.40,
    1.11, 1.05,
    1.65, 0.97,
    0.86, 0.84
  ),
  
# Table 3, p. 796
  emm_post = c(
    20.40, 23.51,
    17.66, 19.80,
    3.02, 3.57,
    2.19, 2.32,
    2.65, 2.16
  ),
  
  se_post = c(
    0.42, 0.47,
    1.19, 1.33,
    0.08, 0.09,
    0.27, 0.31,
    0.10, 0.11
  )

); Haslam2019


# coefficients extracted from table 2, p. 794 
# USED FOR META-ANALYSIS
Haslam2019_est <- tibble(
  
  study = "Haslem 2019",
  
  effect_size = "SMD",
  sd_used = "Pooled pre-test SD - only SDs reported",
  
  main_es_method = "Mixed-effects/multi-level model reg coef",
  
  outcome = c(
   "Loneliness",
   "Depression",
   "Social Anxiety",
   "General practitioner vists",
   "Multiple group memberships"
  ),
  
  analysis_plan = case_when(
      str_detect(outcome, "Loneliness") ~ "Loneliness",
      str_detect(outcome, "Depression") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "Social Anxiety") ~ "All mental health outcomes",
      str_detect(outcome, "General practitioner vists|Multiple group memberships") ~ "Unused outcomes",
      .default = NA_character_
    ),
  
  N_start_t = 66,
  N_start_c = 54,
    
  N_t = N_start_t, 
  N_c = N_start_c, 
  N_total = N_t + N_c, 

  df_ind = N_total,
  
  # I have taken the beta values for the Time X Condition
  b_reg = c(
    -0.743,
    -0.334,
    -0.525,
    -0.752,
     0.523
  ),
  
  se_b_reg = c(
    0.218,
    0.206,
    0.169,
    0.255, 
    0.197
  ),
  
  sd_pre_t = c(3.11, 9.96, 1.11, 1.65, 0.86),
  sd_pre_c = c(2.78, 9.40, 1.05, 0.97, 0.84),
  
  sd_pool = sqrt(((N_t-1)*sd_pre_t^2 + (N_c-1)*sd_pre_c^2)/(N_t + N_c - 2)),
  
  d_reg = b_reg/sd_pool,
  vd_reg = (se_b_reg/sd_pool)^2 + d_reg^2/(2*N_total),
  Wd_reg = (se_b_reg/sd_pool)^2,
  
  J = 1 - 3/(4*df_ind-1),
  
  g_reg = J * d_reg, 
  vg_reg = (se_b_reg/sd_pool)^2 + g_reg^2/(2*N_total),
  Wg_reg = (se_b_reg/sd_pool)^2,
  
  # icc (group-level) from study
  icc = c(.054, .084, .034, .081, .056),
  icc_type = "From study (Table 2, p. 794)",
  
 ) |> 
  rowwise() |> 
  mutate(

  #"In the present study, the program was administered in groups comprising between five and nine participants" (p. 792)
  avg_cl_size = 5, 
  avg_cl_type = "From study (p. 792)",
  
  
  n_covariates = 1,

  # Find info about the function via ?VIVECampbell::gamma_1armcluster
  gamma_sqrt = VIVECampbell::gamma_1armcluster(
    N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
  ),
  
  # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
  df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
  omega = 1 - 3/(4*df_adj-1),
  
  
  gt_reg = omega * d_reg * gamma_sqrt,
  VIVECampbell::vgt_smd_1armcluster(
    N_cl_grp = N_t,
    N_ind_grp = N_c,
    avg_grp_size = avg_cl_size,
    ICC = icc, 
    g = gt_reg, 
    model = "reg_coef",
    cluster_adj = TRUE,
    SE = se_b_reg,
    SD = sd_pool,
    add_name_to_vars = "_reg",
    vars = -var_term1_reg
  ),
  
  vary_id = outcome
  ) |> 
  ungroup(); Haslam2019_est 

```

```{r, echo=FALSE}

dat <- 
  dat |> 
  bind_rows(Haslam2019_est)

```

### **Hilden et al. [-@Hilden2021] (Quality-checked)**

Entering data from Table 2 (p. 181)

```{r Hilden et al 2021}
# Table 2, p. 181
Hilden2021 <- tibble(
  outcome = rep(c(
    "BSL-23 total",   
    "Overall Anxiety Severity and Impairment scale",  
    "Patient Health Questionnaire 9 scale",    
    "Alcohol Use Disorders Identification Test scale",
    "Sheehan disability work or studylife",
    "Sheehan disability social life",
    "Sheehan disability family life"
  ), each = 2, 1), 
  
  
  group = rep(c(
    "Schema therapy group",
    "TAU"
  ), each = 1, 7), 
  
  N = rep(c(23,12), each = 1, 7), 
  N_start = rep(c(25,12), each = 1, 7),
  
  m_pre = c(
    39., 55.7, 
    11.3, 13.2, 
    14, 16.3,
    6.4, 8.4,
    6., 6.8,
    5.2, 6.7,
    5.2, 6.7),
  
  sd_pre = c(
    15.1, 14.9, 
    3.8, 2.6, 
    5.7, 4.1,
    4.8, 5.6,
    3.1, 2.3,
    2.8, 1.6,
    2.8, 1.6),
  
  m_post = c(
    32., 42.6, 
    10.3, 11.4, 
    5.7, 9.2,
    11.9, 14.3,
    5.2, 6.9,
    5.6, 5.9,
    4.9, 6.3),
  
  sd_post = c(
    16.4, 18.8, 
    3.9, 3.5, 
    5.5, 8.2,
    5.2, 5.9,
    3.3, 2.4,
    2.6, 2.7,
    2.9, 2.1)
  
)



# Making the tibble into wide format
Hilden2021_wide <-
  Hilden2021 |> 
  mutate(group = case_when(
    group == "Schema therapy group" ~ "t", 
    group == "TAU" ~ "c",
    TRUE ~ NA_character_
  )) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )

Hilden2021_est <-           
  Hilden2021_wide |>
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "BSL-23 total") ~ "All mental health outcomes",
      str_detect(outcome, "Overall Anxiety") ~ "All mental health outcomes/Anxiety",
      str_detect(outcome, "Patient Health") ~ "Physical health",
      str_detect(outcome, "Alcohol Use Disorders") ~ "Alcohol and drug abuse/misuse",
      str_detect(outcome, "Sheehan disability") ~ "Social functioning (degree of impairment)",
      TRUE ~ NA_character_
    ),
    
    study = "Hilden et al. 2021",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor is imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # For Social functioning lower scores is beneficial, hence these are reverted
    m_post = if_else(outcome %in% c("BSL-23 total", 
                                    "Patient Health Questionnaire 9 scale",
                                    "Overall Anxiety Severity and Impairment scale",
                                    "Alcohol Use Disorders Identification Test scale"), 
                     (m_post_t - m_post_c) * -1,
                     m_post_t - m_post_c), 
    
    sd_pool = sqrt(((N_t - 1) * sd_post_t^2 + (N_c - 1) * sd_post_c^2) / (N_t + N_c - 2)),  
    
    d_post = m_post / sd_pool, 
    vd_post = (1 / N_t + 1 / N_c) + d_post^2 / (2 * df_ind),
    Wd_post = (1 / N_t + 1 / N_c),
    
    J = 1 - 3 / (4 * df_ind - 1),
    
    g_post = J * d_post,
    vg_post = (1 / N_t + 1 / N_c) + g_post^2 / (2 * df_ind),
    Wg_post = Wd_post,
    
    # For Social functioning lower scores is beneficial, hence these are reverted
    m_diff_t = if_else(outcome %in% c("BSL-23 total", 
                                      "Patient Health Questionnaire 9 scale",
                                      "Overall Anxiety Severity and Impairment scale",
                                      "Alcohol Use Disorders Identification Test scale"),
                       (m_post_t - m_pre_t) * -1,
                       m_post_t - m_pre_t),
    
    m_diff_c = if_else(outcome %in% c("BSL-23 total", 
                                      "Patient Health Questionnaire 9 scale",
                                      "Overall Anxiety Severity and Impairment scale",
                                      "Alcohol Use Disorders Identification Test scale"),
                       (m_post_c - m_pre_c) * -1,
                       m_post_c - m_pre_c), 
    
    d_DD = (m_diff_t - m_diff_c) / sd_pool,
    vd_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + d_DD^2 / (2 * df_ind),
    Wd_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + g_DD^2 / (2 * df_ind),
    Wg_DD = Wd_DD
  ) |> 
  rowwise() |>
  mutate(
    
    # Average cluster size in treatment group
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 177)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3 / (4 * df_adj - 1),
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c("vgt_post", "Wgt_post")
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -c("var_term1_DD")
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "9"),
      omega * ((m_diff_t - m_diff_c)/PHQ_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*PHQ_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
  ) |> 
  ungroup() |> 
  mutate(
    vary_id = outcome
  )

Hilden2021_est
  
```

```{r, echo=FALSE}
# Adding Hilden to data
dat <- 
  dat |> 
  bind_rows(Hilden2021_est) 
```

### **Himle et al. [-@Himle2014] (Quality-checked)**

Entering data from Table 2 (p. 174)

```{r Himle et al 2014}
# From table 2, p. 174
Himle2014 <- 
  tibble(
    group = as.factor(rep(c("Intervention group",
                            "Control group"),
                          each = 1, 12)), 
    
    timing = rep(c("Post","3m"), each = 12,1), 
    
    
    outcome = rep(c(
      "LSAS -Total", 
      #"Work-related social anxiety- total", 
      "Brief fear of negative evaluation", 
      "Mini social phobia inventory", 
      "Beck anxiety inventory (BAI)", 
      "PHQ9 depression screen", 
      "Sheehan disability scale"
      #  "Social phobia symptom severity", 
      #  "Clinician global impressions - symptom severity" 
    ), each = 2, 2), 
    
    N = 29,
    
    N_start = 29,
    
    m_pre = rep(c(
      84.31, 87.59, 
      #  18.72, 19.72,
      46.59, 45.,
      6.72, 7.52,
      17.69, 22.9,
      10.59, 12.17,
      5.93, 6.07
      #  2.07, 2.1,
      # 4.59, 4.24)
    ),
    each = 1,2),
    
    sd_pre = rep(c(
      31.51, 26.33, 
      #   7.42, 6.53,
      12.83, 13.19,
      2.2, 2.87,
      12.83, 14.73,
      8.08, 5.62,
      2.6, 2.33
      #  .65, .62,
      #  1.52, .99)
    ),
    each = 1,2),
    
    m_post = c(
      
      #Postvalues
      66.72, 90.62, 
      #  13.21, 19.76,
      39.01, 43.07,
      7.79, 9.66,
      11.34, 24.72,
      5.62, 10.41,
      3.63, 4.77,
      #  1.31, 2.17,
      #  3.76, 4.14,
      
      #3m follow-up
      65.31, 92.79, 
      # 13.31, 19.31,
      35.62, 44.,
      7.48, 10.28,
      12.83, 22.45,
      5.83, 10.72,
      3.48, 5.61
      #  1.28, 2.21,
      #  3.21, 4.21
    ),
    
    ## Standard deviation-values 
    sd_post = c(
      # Post SD values
      29.02, 36.67, 
      # 7.10, 7.83,
      13.57, 14.48,
      2.81, 2.58,
      9.54, 17.10,
      5.49, 6.92,
      2.1, 2.02,
      #  .81, .66,
      #  1.35, 1.27,
      
      37.44, 36.26, 
      #  8.38, 7.72,
      13.57, 12.57,
      3.01, 1.28,
      13.4, 14.96,
      5.74, 6.68,
      2.27, 2.38
      #    .84, .73,
      #    1.35, 1.37
    )
  )

Himle2014_wide <- 
  Himle2014|> 
  mutate(group = case_match(group, "Intervention group" ~ "t", "Control group" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  ) |> 
  relocate(outcome)

# Multi level effect estimates can be obtained from table 3, p. 3
Himle2014_est <- 
  Himle2014_wide |> 
  mutate(
    # analysis_plan = rep(c(
    #  "the Liebowitz Social Anxiety Scale",
    # "The Brief Fear of Negative Evaluation scale",
    #  "The Mini Social Phobia Inventory",
    #  "Beck Anxiety Inventory",
    #  "Patient Health Questionnaire (PHQ-9)",
    #  "Shehan disability scale"
    # ), each = 2),  # assuming there are two rows per each analysis_plan, adjust if necessary
    
    analysis_plan = case_when(
      str_detect(outcome, "LSAS|Mini|Brief") ~ "All mental health outcomes",
      str_detect(outcome, "Beck") ~ "All mental health outcomes/Anxiety",
      str_detect(outcome, "Sheehan") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "PHQ9") ~ "Physical health",
      TRUE ~ NA_character_
    ),
    
    study = "Himle et al. 2014"
    
  ) |> 
  mutate(
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor is imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # MHV: ER VI HELT SIKRE PÅ DETTE?
    # Reverting all outcomes 
    m_post = (m_post_t - m_post_c) * -1,
    
    sd_pool = sqrt(((N_t - 1) * sd_post_t^2 + (N_c - 1) * sd_post_c^2) / (N_t + N_c - 2)),  
    
    d_post = m_post / sd_pool, 
    vd_post = (1 / N_t + 1 / N_c) + d_post^2 / (2 * df_ind),
    Wd_post = (1 / N_t + 1 / N_c),
    
    J = 1 - 3 / (4 * df_ind - 1),
    
    g_post = J * d_post,
    vg_post = (1 / N_t + 1 / N_c) + g_post^2 / (2 * df_ind),
    Wg_post = Wd_post,
    
    # Reverting all outcomes
    m_diff_t = (m_post_t - m_pre_t) * -1,
    m_diff_c = (m_post_c - m_pre_c) * -1,
    
    d_DD = (m_diff_t - m_diff_c) / sd_pool,
    vd_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + d_DD^2 / (2 * df_ind),
    Wd_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + g_DD^2 / (2 * df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 170)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3 / (4 * df_adj - 1),
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c("vgt_post", "Wgt_post")
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -c("var_term1_DD")
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "9"),
      omega * ((m_diff_t - m_diff_c)/PHQ_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*PHQ_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
  ) |> 
  ungroup() |> 
  mutate(
    vary_id = paste0(outcome, "/", timing)
  )

Himle2014_est
```

```{r, echo=FALSE}
# Adding Himle to data
dat <- 
  dat |> 
  bind_rows(Himle2014_est) 
```

### **Izquierdo et al. [-@Izquierdo2021] (Excluded because flawed result reproted. See calculations below)**

Entering data from Table 3 (p. 10) and Table 4 (p. 13)

```{r Izquierdo et al 2021}
# Rows 1-10 is data from Table 3, p. 10 and rows 11-22 is from Table 4 p. 13. 

izquierdo2021 <- tibble(
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,12)),
  timing = rep(c(
    "Post",
    "3m",
    "6m"
  ),
  each = 8,1),
  
  
  outcome = rep(c(
                  #"EE",
                  #"OE",
                  "CS", 
                  "ACI",
                  "SCI",
                  #"POSIT",
                  #"NEGAT",
                  #"AFFECT",
                  #"DISOR",
                  #"DISORG",
                  "Total_score"),
                each = 2,3), 
  
  N = rep(c(7)),
  
  
  m_pre = rep(c(
    # 6.57, 10.57, # EE
    # 6.86, 8.29,  # OE
    13.43, 18.86,# CS
    NA, NA,      # ACI - Basline data not reported
    NA, NA,      # SCI - Basline data not reported
    # 21.29, 15.14,# POSIT
    # 10.57, 6.86, # NEGAT
    # 35.86, 37.14,# AFFECT
    # 5.00, 3.86,  # DISOR
    # 4.86, 2.43,  # DISORG
    77.57, 65.43 # Total_score
    ), each = 1,3),
  
  sd_pre_reported = rep(c(
    # 2.44, 3.36,
    # 2.19, 1.38,
    2.44, 4.34,
    NA, NA,
    NA, NA,
    # 11.09, 5.84,
    # 4.24, 2.54,
    # 5.64, 4.56,
    # 3.00, 3.18, 
    # 2.67, 1.90,
    18.30, 10.85), 
    each = 1,3),
  
  sd_pre = sd_pre_reported *sqrt(7),
  
  m_post = c(
    
    # post measurement
    # 17.86, 10.57,
    # 16.29, 8.14,
    34.14, 18.71,
    36.57, 11.14,
    4.57, 1.14,
    # 8.14, 15.71,
    # 5.43, 7.71,
    # 16.86, 37.00,
    # 2.43, 3.86,
    # 1.29, 1.57,
    34.14, 65.86,
    
    # 3 months measurement
    #  16.00, 9.71,
    #  16.29, 7.57,
    32.29, 17.29,
    37.57, 11.00,
    4.57, 1.00,
    #  7.14, 15.71,
    #  5.00, 8.00,
    #  13.29, 35.14,
    #  2.57, 3.71,
    #  1.00, 1.14,
    29.00, 63.71,
    
    
    # 6 months measurement
    # 16.71, 9.71,
    # 15.71, 6.86,
    32.43, 16.57,
   37.14, 11.57,
    4.29, 1.14,
    #  7.29, 15.29,
    #  5.00, 8.29,
    #  13.00, 36.14,
    #  2.29, 3.57,
    #  1.00, 1.29,
    28.57, 64.57
    
  ),
  
  sd_post_reported = c(
  
  # post measurement
  #  1.86, 3.15,
  #  3.09, 1.35,
    4.81, 2.87,
    5.32, 3.19,
    0.54, 0.38,
  #  2.12, 6.40,
  #  1.27, 3.45,
  #  2.27, 3.74,
  #  0.53, 2.54,
  #  0.49, 0.79,
    3.13, 11.13,
  
  # 3 months measurement 
  #  3.21, 2.98,
  #  2.63, 2.07,
  5.28, 2.87,
  4.20, 3.21,
  0.53, 0.00,
  #  1.07, 6.97,
  #  0.82, 3.56,
  #  0.95, 4.70,
  #  0.79, 2.63,
  #  0.00, 0.38,
  1.83, 11.97,
  
  # 6 months measurement 
  # 16.71, 9.71,
  # 15.71, 6.86,
  5.19, 2.88,
  4.88, 2.94,
  0.76, 0.38,
  #  7.29, 15.29,
  #  5.00, 8.29,
  #  13.00, 36.14,
  #  2.29, 3.57,
  #  1.00, 1.29,
  1.81, 9.68
  ),
  
  sd_post = sd_post_reported * sqrt(7)
  

); izquierdo2021

# Turning data into wide format
params <- tibble(
  filter_val1 = rep(c("Post", paste0(c(3, 6), "m")), 1)
  
)



wide_izquierdo2021_func <- 
  function(filter_val1){
    
    izquierdo2021 |> 
      filter(timing == filter_val1) |> 
      mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
      )   
    
  }



izquierdo2021_est <- 
  pmap(params, wide_izquierdo2021_func) |>
  list_rbind() |> 
  mutate(
    analysis_plan = rep(
      c("Hope, Empowerment & Self-efficacy",
        "Social functioning (degree of impairment)",
        "Social functioning (degree of impairment)",
        "All mental health outcomes" # Total score
      ), each = 1,3),
    
    study = "Izquierdo et al. 2021",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Reverting m_post only for Total_score
    m_post = if_else(outcome == "Total_score", (m_post_t - m_post_c) * -1, m_post_t - m_post_c), 
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    #sd_pool = sqrt(((N_t-1)*sd_post_reported_t^2 + (N_c-1)*sd_post_reported_c^2)/(N_t + N_c - 2)),
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    vary_id = paste0(outcome, "/", timing)
    
  ) |> 
  ungroup()
```

### **Jacob et al. [-@Jacob2010] (Quality-checked)**

Entering data from Table 2 (p. 383)

```{r Jacob et al 2010}
Jacob2010 <- tibble(
  
  group = as.factor(rep(c("Intervention", "Control"), each = 1, 10)), 
  
  timing = rep(c("Post","6m"), each = 10,1),
  
  outcome = rep(c(
    "RSES",   
    "SSES Achievement",  
    "SSES Social",    
    "SSES Appearance",
    "BDI"), each = 2, 2), 
  

  N = rep(c(19, 24), each = 1,10),
  N_start = rep(c(19, 24), each = 1,10),

  m_pre = rep(c(
    13.3, 13.3, # Social Cognition Screening Questionnaire
    18.7, 19.2, # Brief Assessment of Cognition in Schizophrenia
    13.8, 16.3, # Positive and Negative Syndrome Scale
    11.7, 10.8, # Global Assessment of Functioning
    27.8, 30.0) # Social Functioning Scale
    , each = 1,2),
  
  
  sd_pre = rep(c(
    8.6, 9.9,
    4., 6.6,
    6.6, 7.4,
    5.7, 5.2,
    11.8, 12.2)
    , each = 1,2),
  
  
  m_post = c(
    # Post measurement
    16.7, 11.6, 
    20.7, 18.1, 
    14.5, 16., 
    11.6, 10.4,
    22.6, 30.9,
    
    # 6 months measurement
    17.8, 13.2,
    21.5, 19.1,
    15.3, 16.,
    11.7, 11.5,
    22.6, 29.6),
  
  sd_post = c(
    # Post measurement
    10.3, 9.3, 
    6.3, 6.9,
    7.0, 7.2, 
    5.4, 5.3,
    13.1, 12.3,
    
    # 6 months measurement
    11., 11.2,
    6.4, 6.3,
    8., 7.4,
    5.2, 5.7,
    14.5, 13.3)
  
  ) |> 
relocate(outcome, .after = group); Jacob2010

# Making the druss2018 tibble wide in order to estimate the effect sizes and
# further analysis. 

# Turning data into wide format
Jacob2010_wide <- 
  Jacob2010 |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )   


Jacob2010_est <- 
  Jacob2010_wide |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "BDI") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "RSES|SSES") ~ "Self-esteem",
      .default = NA_character_
    ),
    
    study = c("Jacob et al. 2010"),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Reverting all outcomes
   # Reverting m_post for specific outcomes
    m_post = if_else(outcome == "BDI", (m_post_t - m_post_c) * -1, m_post_t - m_post_c),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
   
   
    # Reverting m_post for specific outcomes: BDI
    m_diff_t = if_else(outcome == "BDI",
                       (m_post_t - m_pre_t)*-1,
                        m_post_t - m_pre_t),
    m_diff_c = if_else(outcome == "BDI",
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)  + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) ,
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)  + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD 
    
  ) |> 
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # Average cluster size in treatment group
    # Group size was between 5 to 7 (p. 379)
    
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 379)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
  ) |> 
  ungroup() |> 
  mutate(vary_id = paste0(outcome, "/", timing));  Jacob2010_est
```

```{r, echo=FALSE}
# Adding Jacob to data
dat <- 
  dat |> 
  bind_rows(Jacob2010_est) 
```

### **James et al. [-@James2004] (Quality-checked)**

Entering data from Table 2 (p. 988)

```{r James et al 2004}
James2004 <- tibble(
  group = rep(c("Treatment", "Control"), each = 1,5),
 
  outcome =  rep(c("BPRS", "BSI-Global","DAST", "AUDIT","SDS"
  ), each = 2,1), 
  
  N = c(
    29, 29,
    31, 29,
    29,29,
    29, 29,
    28, 29
    
  ), 
  
  N_start = rep(c(
    32,31
  ), each = 1,5),
  
  m_pre = c(
    35.41, 33.93, # BPRS
    1.36, 1.27, # BSI-Global
    11.58,  9.10, # DAST
    11.65, 12.65, # AUDIT
    7.42, 6.75    # SDS
  ), 
  sd_pre = c(
    11.08, 8.61,
    0.88, 0.63,
    4.82, 4.66,
    10.19, 8.22,
    3.63, 3.46
  ),
  m_post = c(
    30.48, 38.10,
    0.87, 1.12,
    4.96, 8.24,
    8.34, 11.65,
    3.85, 5.20
  ),
  sd_post = c(
    10.68, 11.54,
    0.63, 0.77,
    4.49, 5.31, 
    8.35, 7.35,
    3.30, 3.85
  )
)


# Turning data into wide format
James2004_wide <- James2004 |> 
      mutate(group = case_match(group, "Treatment" ~ "t", "Control" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
      ); James2004_wide




James2004_est <- James2004_wide |> 
  mutate(
    
    study = "James et al. 2004",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    analysis_plan = case_when(
      str_detect(outcome, "BPRS|BSI-Global") ~ "All mental health outcomes",
      str_detect(outcome, "DAST|AUDIT|SDS") ~ "Alcohol and drug abuse/misuse",
      .default = NA_character_
    ),
    
    # F-values taken from table 2, p. 988. (Not used. Yields larger varainces)
    F_val = c(
      7.364, 0.865, 16.221, 1.352, 2.205
    ),
    
    ppcor = ppcor_imp,  
    ppcor_method = "Imputed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    # For all scores lower are benefical
    m_post = (m_post_t - m_post_c)*-1,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c)
    
  ) |> 
rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group was approx. 6, p. 985
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 985)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
      n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
   
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
  
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
       vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); James2004_est

```

```{r, echo=FALSE}
# Adding James to data
dat <- 
  dat |> 
  bind_rows(James2004_est)

```

### **Kanie et al. [-@Kanie2019] (Quality-checked)**

Entering data from Table 4 (p. 8)

```{r Kanie et al 2019}
# Taken from table 4, p. 8
Kanie2019 <- tibble(
  group = as.factor(rep(c("SCIT", "TAU"), each = 1,5)), 
  
  timing = "Post", 
  
  outcome = rep(c(
    "SCSQ total", # Social Cognition Screening Questionnaire
    "BACS", # Brief Assessment of Cognition in Schizophrenia
    "PANSS total", # Positive and Negative Syndrome Scale
    "GAF", # Global Assessment of Functioning
    "SFS" # Social Functioning Scale
  ),  each = 2,1), 
  
  N = rep(c(32, 29), 5),            
  
  N_start = 36,
  
  m_pre = c(
    31.04, 32.83, # Social Cognition Screening Questionnaire
    -1.64, -1.33, # Brief Assessment of Cognition in Schizophrenia
    64.78, 63.78, # Positive and Negative Syndrome Scale
    51.59, 53.44, # Global Assessment of Functioning
    116.06, 106.53 # Social Functioning Scale
    ), 
    
  sd_pre = c(
    3.58, 3.47, 
    1.01, 1.42, 
    18.18, 18.98, 
    8.81, 9.49,
    23.95, 23.47), 
  
  m_post = c(
    # 3 months measurement - This is a midterm measurement/during-the-intervention measure 
  #  32.94, 33.45, 
  #  -1.37, -1.16, 
  #  61.34, 59.94, 
  #  52.25, 58.06,
  #  118.44, 107.41,
    
    
    # 6 months measurement
    32.79, 33.4,
    -1.13, -0.77,
    60.03, 59.81,
    56.09, 55.59,
    117.45, 108.91
    ),
  
  sd_post = c(
    # 3 months measurement This is a midterm measurement/during-the-intervention measure
  #  3.2, 2.63, 
  #  1.09, 1.36,
  #  19.15, 18.02, 
  #  13.63, 13.47,
  #  25.31, 28.03,
    
    # 6 months measurement
    3.17, 2.63,
    1.22, 1.35,
    18.68, 17.32,
    15.33, 14.5,
    25.42, 26.6
    )
  
); Kanie2019


# Turning data into wide format
Kanie2019_wide <- Kanie2019 |> 
      mutate(group = case_match(group, "SCIT" ~ "t", "TAU" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
      ) |> 
  mutate(
    es_paper = c(0.33, -0.05, -0.04, 0.26, -0.04),
    es_paper_type = "DiD"
  ); Kanie2019_wide
    

Kanie2019_est <- 
  Kanie2019_wide |> 
  mutate(
    
    study = "Kanie et al. 2019",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    analysis_plan = case_when(
      str_detect(outcome, "SCSQ") ~ "Unused",
      str_detect(outcome, "BACS") ~ "All mental health outcomes",
      str_detect(outcome, "SFS|GAF") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "PANSS") ~ "All mental health outcomes/Symptoms of psychosis",
      .default = NA_character_
    ),

    # ppcor is imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    # For PANSS: reverted
    m_post = if_else(
      outcome != "PANSS total", 
      (m_post_t - m_post_c)*-1,
      m_post_t - m_post_c
    ),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For PANSS: reverted
    m_diff_t = if_else(
      outcome != "PANSS total",
      (m_post_t - m_pre_t)*-1,
      m_post_t - m_pre_t
    ),
    m_diff_c = if_else(
      outcome != "PANSS total",
      (m_post_c - m_pre_c)*-1,
      m_post_c - m_pre_c
    ),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |>
  mutate(
    
    # Average cluster size in treatment group
    # Group size was between 4 to 8 (p. 3)
    
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 3)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * ((m_diff_t - m_diff_c)/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste0(outcome)
  ) |> 
  ungroup(); Kanie2019_est

```

```{r, echo=FALSE}

dat <- 
  dat |> 
  bind_rows(Kanie2019_est)

```

### **Lim et al. [-@Lim2020] (Quality-checked)**

Entering data from Table 2 (p. 5)

```{r Lim et al 2020}
# Data from table 2 (p.5)  containing means, standard deviation


Lim2020 <- tibble(
  group = as.factor(rep(c("Social Cognitive Skills Training",
                          "Treatment as Usual"), each = 1,6)),
  
  outcome = as.factor(rep(c(
                            "Social Functioning (QLS)",
                            "PANSS Negative",
                            "PANSS Excitement",
                            "PANSS Cognitive",
                            "PANSS Positive",
                            "PANSS Depressive"
                            ), each = 2,1 )
                      ),
  
  N = rep(c(18, 21), 6),
  N_start = rep(c(21, 24), 6),
  
  
  m_pre = c(46.15, 61.74, # Social Functioning (QLS) total
           18.22, 16.57,  # PANSS Negative
           6.28, 6.91,    # PANSS Excitement
           13.94,13.57,   # PANSS Cognitive
           7.67, 9.62,    # PANSS Positive
           8.67, 11.29    # PANSS Depressive
           
    ), 
  
  sd_pre = c(12.01, 18.60, # Social Functioning (QLS) total
             6.23, 3.84,   # PANSS Negative
             1.87, 2.02,   # PANSS Excitement
             4.67, 3.25,   # PANSS Cognitive
             2.00, 3.54,   # PANSS Positive
             2.47, 3.45    # PANSS Depressive
  ),
  
  m_post = c(54.76, 57.70, # Social Functioning (QLS) total
             16.89, 18.14, # PANSS Negative
             6.89, 7.33,   # PANSS Excitement
             13.44, 15.19, # PANSS Cognitive
             8.72, 8.72,   # PANSS Positive
             8.94, 13.81   # PANSS Depressive
    ),
  
  sd_post = c(10.88, 15.92, # Social Functioning (QLS) total
              4.51, 3.71,   # PANSS Negative
              2.05, 2.20,   # PANSS Excitement
              2.75, 2.98,   # PANSS Cognitive
              3.06, 2.67,   # PANSS Positive
              2.98, 4.21    # PANSS Depressive
    
  )
)

# Making the lim2020 tibble wide in order to estimate the effect sizes and
# further analysis. 


Lim2020_wide <-
  Lim2020 |> 
  mutate (group = case_match(
    group, "Social Cognitive Skills Training"  ~ "t", 
           "Treatment as Usual" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


# Effect size calculating 
Lim2020_est <-           
  Lim2020_wide |>
  mutate(
    
    study = "Lim et al. 2020",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    analysis_plan = case_when(
      str_detect(outcome, "QLS") ~ "Wellbeing and Quality of Life",
      str_detect(outcome, "Negative") ~ "All mental health outcomes/Symptoms of psychosis",
      
      .default = "All mental health outcomes/Symptoms of psychosis"
    ),
    
    # ppcor is imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    # F-Values - based on ANOVA - from table 2 (p.5)
    #F_val = c(3.116, 5.157, 0.085, 5.055, 1.528, 8.487),
    
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # For Social functioning lower scores is beneficial why these is reverted
    m_post = if_else(outcome != "Social Functioning (QLS)", (m_post_t - m_post_c)*-1,
                     m_post_t - m_post_c), 
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    m_diff_t = if_else(outcome != "Social Functioning (QLS)", 
                       (m_post_t - m_pre_t) * -1,
                       m_post_t - m_pre_t),
    
    m_diff_c = if_else(outcome != "Social Functioning (QLS)",
                       (m_post_c - m_pre_c) * -1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
    
  ) |>
  rowwise() |> 
  mutate(
    
    icc = ICC_01,
    icc_type = "Imputed",
    
    # Average cluster size in treatment group
    # We don't know the average group size. Therefore, we impute this value
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
       vars = c(vgt_post, Wgt_post)
      ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor  = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
      ),
    
    vary_id = outcome
  ) |> 
  ungroup(); Lim2020_est
```

```{r, echo=FALSE}
# Adding Lim to data
dat <- 
  dat |> 
  bind_rows(Lim2020_est) 
```

### **Lloyd-Evans et al. [-@Lloyd-Evans2020] (Quality-checked)**

Entering data from Table 2 (p. 9)

```{r Lloyd-Evans et al 2020}
# Dataextraction from Lloyd-Evans (2020)
# Attempt from Jakob

# Loading the relevant package 
library(estmeansd)

# From Table 2 on page 9

# Creating a tibble containing data with IQR, and later i will estimate the mean 
# and SD from this information using bc.mean.sd (Box-Cox method) 

lloyd_Evans2020_IQR <- tibble(
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,5)),
  
  outcome = as.factor(rep(c(
                            "DJG-total",
                            #"DJG-social", removed
                            #"DJG-Emotional", 
                            "GAD-7", 
                            "WEMWBS", 
                            "LSNS6", 
                            "RGUK" 
                            #"ReQoL-10", 
                            #"EQ-VAS", 
                            #"days in acute", removed
                            #"inpatient bed days", removed
                            # "kept appointments"
                            #"missed appointments" removed
                            
# DJG-social, days-in-acute, inpatient bed days, missed appointments: are all 
# removed because there was not enough variance between the IQR to get a an estimate
# Their numbers are therefore also removed further down. 
                            
                            
                          ),
                          each = 2,1)
  ),
  
  N_start = rep(c(30,10),
          each = 1,5),
  
  q1_pre = c(
    10, 9,   #  Loneliness: De Jong-Gierveld (DJG) Loneliness Scale
    # 5, 4,
    # 5, 5,    # DJG: Emotional Loneliness subscale score
    15, 11,  # Generalized Anxiety Disorder Questionnaire (GAD-7) 
    20, 24,  # Warwick-Edinburgh Mental Well-being Scale (WEMWBS) 
    4, 9,    # Social network: Lubben Social Network scale (LSNS6)
    5, 8.8  # Perceived social capital: Resource Generator UK (RGUK)
    # 4, 5,    # Recovering Quality of Life Questionnaire (ReQoL-10)
    # 29, 30,  # Self-rated health using EQ-Visual Analogue Scale (EQ VAS)
    # 0, 0,
    # 0, 0,
    # 3, 1     # Community service kept appointments
    # 0, 0
  ), 
  
  median_pre = c(
    11, 10.5,
    # 5, 5,
    # 6, 6,
    19, 16,
    26.5, 30,
    7, 11.5,
    9.5, 13
   # 9, 9.5
   # 35, 47.5,
   # 0, 0,
   # 0, 0,
   # 6.5, 6.5
   # 0, 0
  ),
  
  q3_pre = c(
    11, 11,
   # 5, 5,
   # 6, 6,
    21, 18,
    32, 34,
    9, 15,
    12, 18.3
   # 14, 15,
   # 50, 50, 
   # 0,0,
   # 0,0,
   # 11, 17
   # 1, 1
  ),
  
  N = rep(c(25,10), # TOT N
               each = 1,5),
  
  q1_post = c(
    8, 7,
    # 4, 4,
    # 4, 4,
    10.5, 11,
    23, 23,
    6, 6,
    6, 6.5
    # 8, 10,
    # 30, 35,
    # 0,0,
    # 0,0,
    # 2, 1
    # 0,0
  ),

  median_post = c(
    9, 10,
    # 5, 4,
    # 5, 6,
    14, 13.5,
    29.5, 31,
    7.5, 11,
    9, 13
    # 14.5, 13.5,
    # 40, 52.5,
    # 0,0,
    # 0,0,
    # 3.5, 8
    # 0, 1.5
    
  ),
  
  q3_post = c(
    11, 11,
    # 5, 5, 
    # 6, 6,
    17.5, 16, 
    34.5, 37,
    11, 15,
    12.3, 22.3
    # 19, 19,
    # 60, 60,
    # 0,0,
    # 0,0,
    # 10, 10
    # 1, 3
  )
) |> 
  rowwise() |> 
  mutate (
    
  pre = list(bc.mean.sd(
    q1.val = q1_pre,
    med.val = median_pre,
    q3.val = q3_pre,
    n = N_start,
    avoid.mc = T
  )[c("est.mean", "est.sd")]),
  
  post = list(bc.mean.sd(
    q1.val = q1_post,
    med.val = median_post,
    q3.val = q3_post,
    n = N,
    avoid.mc = T
  )[c("est.mean", "est.sd")])
  
  ) |> 
  unnest_wider(c(pre, post), names_sep = "_") |> 
  rename(
    
    m_pre = pre_est.mean,
    sd_pre = pre_est.sd,
    m_post = post_est.mean,
    sd_post =  post_est.sd
  
  ) |> 
  suppressWarnings(); lloyd_Evans2020_IQR



# Creating a tibble with the rest of the data which is measured by mean and SD
lloyd_Evans2020_means <- tibble(          
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,1)),
  
  outcome = as.factor(rep(c("PHQ-9"
                           #  "TBD",
                           # "EQ-5D-5L
                          ),
  each = 2,1)
  ),
  
  N_start = rep(c(30,10),
          each = 1,1),
  
  m_pre = c(
    21.6, 21.1   # Depression: Patient Health Questionnaire (PHQ-9)
  #  32.7, 38.4,  # Activity: Time Budget Diary (TBD)
  #  0.283, 0.400 # EuroQol Health Questionnaire (EQ-5D-5L) Index value
  ),
  
  sd_pre = c(
    5.3, 4.5
   # 9.6, 11.2,
   # 0.40, 0.24
  ),
  
  N = rep(c(25,10), # TOT N
               each = 1,1),
  
  m_post = c(
    16.4, 18.8
    # 36.9, 35,
    # 0.472, 0.453
  ),
  
  sd_post = c(
    6.8, 4.8
   # 12.9, 14.4,
   # 0.33, 0.236
    
  )
); lloyd_Evans2020_means


# Combing the obejct so i have the all the data from table 2 in one obejct
lloyd_Evans2020 <- lloyd_Evans2020_IQR |> 
  full_join(lloyd_Evans2020_means); lloyd_Evans2020


# Turning dataset into wide format
lloyd_Evans2020_wide <-
  lloyd_Evans2020 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", 
    "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  )

# Effect size calculating 
lloyd_Evans2020_est <-           
  lloyd_Evans2020_wide |>
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "PHQ-9") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "GAD-7") ~ "All mental health outcomes/Anxiety",
      str_detect(outcome, "DJG-total") ~ "Loneliness",
      str_detect(outcome, "WEMWBS") ~ "Wellbeing and Quality of Life",
      str_detect(outcome, "LSNS6|RGUK") ~ "Unused outcomes",
      .default = NA_character_
    ),
    study = "Lloyd Evans et al. 2020",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor is imputed
     ppcor = ppcor_imp,
     ppcor_method = "Imputed"
  ) |> 
  mutate(
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Reverting m_post for specific outcomes
    m_post = if_else(outcome %in% c("DJG-total", "GAD-7", "PHQ-9"),
                     (m_post_t - m_post_c) * -1, 
                     m_post_t - m_post_c),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # Reverting specific values
   m_diff_t = if_else(outcome %in% c("DJG-total", "GAD-7", "PHQ-9"),
                       (m_post_t - m_pre_t)*-1,
                        m_post_t - m_pre_t),
    m_diff_c = if_else(outcome %in% c("DJG-total", "GAD-7", "PHQ-9"),
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD,
    
    vary_id = outcome
    
  ) |> 
  rowwise() |> 
  mutate(
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "9"),
      omega * ((m_diff_t - m_diff_c)/PHQ_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*PHQ_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
    ) |> 
  ungroup(); lloyd_Evans2020_est

```

```{r, echo=FALSE}
# Adding Lloyed-Evans to data
dat <- 
  dat |> 
  bind_rows(lloyd_Evans2020_est)
```

### **Madigan et al. [-@Madigan2013] (Quality-checked)**

Entering data from Table 2 (p. 141)

```{r Madigan et al 2013}
# Table 2, p. 141
Madigan2013 <- tibble(
  group = rep(c("GPI",
                "TAU"),
              each = 1,12),
  
  outcome = rep(c("ASI", 
                 # "BIS",
                # "Attitude to treatment",
                  "Positive symptoms",
                  "Negative symptoms",
                  "Depressive symptoms",
                  "Global functioning (GAF)",
                  "WHOQOL"), 
                each = 2,2),
  
  timing = rep(c("3m", "12m"), each = 12),
  
  N = c(
    # 3 months
    36, 18,
  #  39, 19,
  #  33, 15,
    42, 22,
    40, 20,
    40, 20,
    39, 19,
    34, 15,
    
    # 12 months
    28, 14,
   # 32, 14,
   # 30, 12,
    32, 17,
    32, 19,
    33, 11,
    31, 16,
    34, 14),
  
  N_start = rep(c(
    59, 29,
  # 47, 19,
  # 39, 17,
    59, 28,
    59, 28,
    59, 28,
    58, 29,
    39, 14
  ), each = 1, 2),
 
  m_pre = rep(c(
    10., 10.1,
  #  6.8, 6.3,
  #  5.8, 5.8,
    5.4, 5.7,
    7.7, 7.4,
    5.1, 5.,
    38.3, 38.,
    12.5, 13.3),
    each = 1,2),
  
  
  sd_pre = rep(c(
    3.6, 3.7,
   # 2.8, 2.7,
   # 9.6, 9.7, 
    4., 4.8,
    3.1, 3.,
    5.7, 6.4,
    13.1, 9.,
    4., 2.8),
    each = 1,2),
  
  m_post = c(
    # 3 months
    9.9, 10.1,
  #  7.7, 6.6,
  #  8.1, 5.9,
    4.8, 5.1,
    3.8, 3.2,
    4.4, 4.6,
    37.4, 36.6,
    13.5, 12.6,
    
    #12 months
    9.8, 10.1,
   # 7., 6.6,
   # 7., 6.5,
    4.9, 5.1,
    4.6, 4.8,
    4.3, 4.3,
    37.6, 37.2,
    12.6, 11.1
    ),
  
  
  sd_post = c(
    # 3 months
    4., 4.2,
  #  2.2, 2.4,
  #  9.9, 9.6,
    3.7, 4.1,
    2.4, 2.3,
    4.3, 4.8,
    8., 9.6,
    3.3, 3.4,
    
    #12 months
    3.9, 4.,
  #  2.9, 1.5,
  #  9.5, 9.3,
    4., 4.2,
    3., 3.2,
    4.4, 4.2,
    8.34, 11.5,
    3.4, 2.9)
  
); Madigan2013


# Turning data into wide format
Madigan2013_wide <- Madigan2013 |>  
      mutate(group = case_match(group, "GPI" ~ "t", "TAU" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col())

# Multi level effect estimates can be obtained from table 3, p. 3
Madigan2013_est <- 
  Madigan2013_wide |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "ASI") ~ "Alcohol and drug abuse/misuse",
      str_detect(outcome, "Depressive symptoms") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "Positive") ~ "All mental health outcomes",
      str_detect(outcome, "Negative") ~ "All mental health outcomes/Negative symptoms",
      str_detect(outcome, "Global") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "WHOQOL") ~ "Wellbeing and Quality of Life",
      .default = NA_character_
    ),
    
    
    study = c("Madigan et al. 2013"),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    
  ) |> 
  mutate(
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
  
   # Reverting m_post for specific outcomes
    m_post = if_else(outcome %in% c("ASI", 
                                    "Positive symptoms", 
                                    "Negative symptoms",
                                    "Depressive symptoms"),
                     (m_post_t - m_post_c) * -1, 
                     m_post_t - m_post_c),
    
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
   
   
   # Reverting specific values
   m_diff_t = if_else(outcome %in% c("ASI", 
                                    "Positive symptoms", 
                                    "Negative symptoms",
                                    "Depressive symptoms"),
                       (m_post_t - m_pre_t)*-1,
                        m_post_t - m_pre_t),
    m_diff_c = if_else(outcome %in% c("ASI", 
                                    "Positive symptoms", 
                                    "Negative symptoms",
                                    "Depressive symptoms"),
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
  
    
  ) |>
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
  
    
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * ((m_diff_t - m_diff_c)/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste0(outcome, "/", timing)
    
  ) |> 
  ungroup(); Madigan2013_est

```

```{r, echo=FALSE}
dat <-
  dat |> 
  bind_rows(Madigan2013_est)

```

### **McCay et al. [-@McCay2006] (Quality-checked)**

Entering data from Table 2 (p. 109)

```{r}

McCay2006_est <- 
  tibble(
    
    study = "McCay 2006",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    outcome = c("MES", "QLS total", "PANSS Negative", "PANSS total"),
    analysis_plan = c(
      "Self-esteem", "Wellbeing and Quality of Life", 
      "All mental health outcomes/Negative symptoms", "All mental health outcomes/Symptoms of psychosis"
      ),
    
    
    N_t = 26,
    N_c = 14,
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    N_start_t = N_t, 
    N_start_c = N_c,
    
    n_covariates = 1,
    
    m_pre_t = c(78.2, 77.2, 11.7, 26.5),
    sd_pre_t = c(19.8, 14.5, 3.7, 5.3),
    
    m_post_t = c(67.9, 82.5, 10.6, 22.2),
    sd_post_t = c(18.9, 11.6, 3.5, 3.5),
    
    tval_paired_t = c(3.6, -2, 1.6, 4),
    
    r_t =((sd_pre_t^2*tval_paired_t^2 + sd_post_t^2*tval_paired_t^2) - 
           (m_post_t-m_pre_t)^2 * N_t)/ (2 * sd_pre_t*sd_post_t*tval_paired_t^2),
    
    z_t = 0.5 * log( (1+r_t)/(1-r_t) ),
    v_t = 1/(N_t-3),
    w_t = 1/v_t,
    
    m_pre_c = c(69.6, 82.2, 10.6, 24.9),
    sd_pre_c = c(18.9, 13.4, 2.7, 5.3),
    
    m_post_c = c(69.7, 85.9, 10.1, 23.6),
    sd_post_c = c(19.2, 12.1, 3, 6),
    
    tval_paired_c = c(-0.01, -1.5, 0.75, 0.89),
    
    r_c =((sd_pre_c^2*tval_paired_c^2 + sd_post_c^2*tval_paired_c^2) - 
           (m_post_c-m_pre_c)^2 * N_c)/ (2 * sd_pre_c*sd_post_c*tval_paired_c^2)
    
  ) |> 
  mutate(
   
    r_c = if_else(r_c < 0, abs(r_c), r_c),
    
    z_c = 0.5 * log( (1+r_c)/(1-r_c) ),
    v_c = 1/(N_c-3),
    w_c = 1/v_c,
     
    mean_z =  (w_t*z_t + w_c*z_c)/(w_t + w_c),  
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "Calculated from study results",
    
    #  Lower scores on the Modified Engulfment Scale and Positive and Negative Symptoms Scale 
    #indicate less engulfment and fewer symptoms respectively; higher scores on the Quality of 
    # Life Scale indicate improved adjustment
    
    m_post = if_else(str_detect(outcome, "QLS"), (m_post_t - m_post_c), (m_post_t - m_post_c)*-1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For YMRS and HAM-D lower scores are beneficial why these are reverted
    m_diff_t = if_else(str_detect(outcome, "QLS"), (m_post_t - m_pre_t), (m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(str_detect(outcome, "QLS"), (m_post_c - m_pre_c), (m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate( 
    
    # 
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ), 
    
    vary_id = outcome
    
  ) |> 
  ungroup(); McCay2006_est


```

```{r, echo=FALSE}

dat <- 
  dat |> 
  bind_rows(McCay2006_est)

```

### **McCay et al. [-@McCay2007] (Quality-checked)**

Results are extracted from Table 3 (p. 6) and Table 4 (p. 7). Sample size data is retrieved from Table 1 (p. 6)

```{r}
McCay2014_est <-
  tibble(
    
    study = "McCay 2007/2014",
    
    effect_size = "SMD",
    sd_used = "Unknown - d was calculated from ANOVA F-val",
    
    outcome = c("MES", "QLS", "MHS"),
    
    N_t = 29,
    N_c = 18,
    
    N_total = N_t + N_c,
    
    N_start_t = 41, # See text on page 3
    N_start_c = 26,
    
    df_ind = N_total,
    n_covariates = 1,
    
    m_pre_t = c(85.99, 61.90, 137.05),
    sd_pre_t = c(19.38, 17.08, 28.83),
    
    m_post_t = c(77.64, 72.76, 143.52),
    sd_post_t = c(20.28, 15.03, 26.17),
    
    tval_paired_t = c(4.12, -6.59, -2.13),
    
    ppcor = ((sd_pre_t^2*tval_paired_t ^2 + sd_post_t^2*tval_paired_t^2) - 
           (m_post_t-m_pre_t)^2 * N_t)/ (2 * sd_pre_t*sd_post_t*tval_paired_t^2),
    
    ppcor_method = "Calcualted from treatment group scores only",
    
    F_val = c(6.5, 11.67, 6.15),
    main_es_method = "Repeated ANOVA (group x time)",
    
    analysis_plan = c(
      "Self-esteem", "Wellbeing and Quality of Life", 
      "Hope, Empowerment & Self-efficacy"
      ),
    
    d_adj = sqrt((F_val * N_total)/(N_t*N_c)) * sqrt(1-ppcor^2),
    vd_adj = (1/N_t + 1/N_c) * (1-ppcor^2) + d_adj^2/(2*N_total),
    Wd_adj = (1/N_t + 1/N_c) * (1-ppcor^2),
    
    J = 1 - 3/(4*df_ind-1),
    g_adj = J * d_adj,
    vg_adj = (1/N_t + 1/N_c) * (1-ppcor^2) + g_adj^2/(2*N_total),
    Wg_adj = (1/N_t + 1/N_c) * (1-ppcor^2)
    
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # "was 7.0±2.1 (range of three to 12)" (p. 57)
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_adj = omega * d_adj * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t,
      N_ind_grp = N_c,
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_adj, 
      model = "ANCOVA",
      cluster_adj = FALSE,
      R2 = ppcor,
      add_name_to_vars = "_adj",
      vars = -var_term1_adj
    ), 
    vary_id = outcome
    
  ) |> 
  ungroup(); McCay2014_est 

```

```{r, echo=FALSE}

dat <- 
  dat |> 
  bind_rows(McCay2014_est)

```

### **Michalak et al. [-@Michalak2015] (Quality-checked)**

Entering data from Table 2 (p. 957)

```{r Michalak et al 2015}
Michalak_2015 <- tibble(
  outcome = rep(c(
    "HAM-D",   # Hamilton Depression Rating Scale
    "BDI"      # BDI
  ), each = 3), 
  
  group = rep(c(
    "MBCT", # mindfulness-based cognitive therapy
    "CBASP", # cognitive behavioral analysis system of psychotherapy
    "TAU" # Treatment as usual (control group)
  ), times = 2), 
  
  N = rep(c(36, 35, 35), times = 2), 
  N_start = N,
  
  m_pre = c(
    23.03, 24.71, 23.87,   # Hamilton Depression Rating Scale
    31.31, 30.26, 29.76    # BDI
  ),
  
  sd_pre = c(
    6.27, 6.69, 6.33,
    9.55, 7.52, 7.42
  ),
  
  m_post = c(
    17.86, 14.64, 21.16,     # Hamilton Depression Rating Scale
    22.69, 22.28, 28.34      # BDI
  ), 
  
  sd_post = c(
    10.37, 8.85, 8.16,
    12.11, 12.54, 10.27
  )
)


Michalak2015_wide_DD <- 
  map(c("MBCT", "CBASP"), \(x)
      Michalak_2015 |> 
      filter(group %in% c(x, "TAU")) |> 
      mutate(group = case_match(group, x ~ "t", "TAU" ~ "c")) |> 
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
      ) |> 
        mutate(
          treatment = x,
          N_total = N_t + N_c
        ) |> 
        relocate(treatment)
  ) |> 
  list_rbind() |> 
  mutate(
  
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    analysis_plan = "All mental health outcomes/Depression"
  
  )


Michalak2015_wide_from_paper <- 
  tibble(
    
    effect_size = "SMD",
    sd_used = "Unknown - extracted from report effect sizes",
    main_es_method = "Covariate-adjusted es from multi-level regression",
    
    analysis_plan = rep(c(
      "Wellbeing and Quality of Life",
      "Social functioning (degree of impairment)"
    ), c(8,2)),
    
    
    outcome = paste0(
      rep(c("SF-36", "SASS"), c(8,2)), "/", 
      rep(c("Vitality", "Social func", "Role", "Mental health", "overall"), each = 2)
    ),
    
    treatment = rep(c("MBCT","CBASP"), each = 1,5),
    N_t = rep(c(36,35), each = 1,5), 
    N_c = 35, 
    N_total = N_t + N_c, 
    
    N_start_t = N_t,
    N_start_c = N_c,
    
    b_reg = c(1.12, 1.32, 
             0.27, 0.74,
             0.52, 0.46,
             0.63, 1.25,
             2.43, 1.68),
    se_b_reg = c(0.90, 0.70,
             0.43, 0.44,
             0.26, 0.24,
             0.95, 0.89,
             1.01, 0.93),
    
    t_val = b_reg/se_b_reg,
    
    es_paper = c(0.31,0.36, 
                 0.12, 0.34,
                 0.48, 0.42,
                 0.14, 0.28,
                 0.57, 0.40),
    
    es_paper_type = "Covariate-adjusted ES (d) from a multi-level model",
    
    sd_total = b_reg/es_paper
    
  )


Michalak_2015_dat <- 
  bind_rows(Michalak2015_wide_DD, Michalak2015_wide_from_paper)

ppcor <- ppcor_imp <- 0.5

Michalak_2015_est <- 
  Michalak_2015_dat |> 
  mutate(
    study = "Michalak et al. 2015",
    vary_id = paste0(outcome, "/", treatment),
    
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    df_ind = N_total,
    m_post = if_else(is.na(es_paper), (m_post_t - m_post_c)*-1, NA_real_),
    sd_pool = if_else(
      is.na(es_paper),
      sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),
      sqrt( ((N_total-1)/(N_total-2))*sd_total^2 - ((N_t*N_c)/(N_total * (N_total-2)))*b_reg^2 )
    ),
    
    d_post = m_post/sd_pool,
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = if_else(is.na(es_paper), (1/N_t + 1/N_c), NA_real_),
    
    J =  1 - 3/(4*df_ind-1),
    
    g_post =  J * (m_post/sd_pool),
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # DiD (DD) effect sizes for HAM-D og BDI
    
    
    
    #cluster bias adjustment
    
    # For HAM-D lower scores is beneficial, hence these are reverted
    m_diff_t = if_else(
      str_detect(outcome, "HAM|BDI"),
      (m_post_t - m_pre_t) * -1,
      m_post_t - m_pre_t
    ),
    
    m_diff_c = if_else(
      str_detect(outcome, "HAM|BDI"),
      (m_post_c - m_pre_c) * -1,
      m_post_c - m_pre_c
    ), 
    
    d_DD = (m_diff_t - m_diff_c) / sd_pool,
    vd_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + d_DD^2 / (2 * df_ind),
    Wd_DD = if_else(is.na(es_paper), 2 * (1 - ppcor) * (1 / N_t + 1 / N_c), NA_real_),
    
    g_DD = J * d_DD, 
    vg_DD = 2 * (1 - ppcor) * (1 / N_t + 1 / N_c) + g_DD^2 / (2 * df_ind),
    Wg_DD = Wd_DD,
    
    # Reg adjusted effect sizes for SF-36 and SASS outcomes
    d_reg = b_reg/sd_pool,
    vd_reg = d_reg^2 * (1/t_val^2 + 1/(2*df_ind)),
    Wd_reg = d_reg^2 * (1/t_val^2),
    
    g_reg = J * d_reg,
    vg_reg = g_reg^2 * (1/t_val^2 + 1/(2*df_ind)),
    Wg_reg = Wd_reg
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    avg_cl_size = 6, 
    avg_cl_type = "From study (p. 177)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,# Baseline scores only
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3 / (4 * df_adj - 1),
    
    # Cluster-adjusting g_post
    gt_post = if_else(is.na(es_paper), omega * d_post * gamma_sqrt, NA_real_),
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c("vgt_post", "Wgt_post")
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -c("var_term1_DD")
    ),
    
    gt_reg = omega * d_reg * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_reg, 
      model = "reg_coef",
      cluster_adj = TRUE,
      t_val = t_val,
      q = n_covariates,
      add_name_to_vars = "_reg",
      vars = -c("var_term1_reg")
    ),
    
    across(c(Wgt_post, Wgt_DD:adj_value_DD), ~ if_else(!str_detect(outcome, "HAM|BDI"), NA, .x)),
    across(c(Wgt_reg:adj_value_reg), ~ if_else(str_detect(outcome, "HAM|BDI"), NA, .x)),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      )
    
  ) |> 
  ungroup(); Michalak_2015_est


```

```{r, echo=FALSE}
# Adding Michalak to data
dat <- 
  dat |> 
  bind_rows(Michalak_2015_est) 
```

### **Morley et al. [-@Morley2014] (Quality-checked)**

Entering data from Table 2 (p. 136)

```{r Morley et al 2014}
# Data from table 2 containing means and standard deviation on page 136

Morley2014 <- tibble(
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,4)),
  
  outcome = as.factor(rep(c("BSS",
                            "HADS_depression",
                            "HADS_anxiety",
                            "SES"), each = 2,1
  )),
  
  N = rep(c(
    122, 63 # obtained from Table 1 (p. 135)
  ), each = 1,4),
  
  
   N_start = rep(c(
    122, 63 # obtained from Table 1 (p. 135)
  ), each = 1,4),
  
#   q = 6, # Controlling for Unemployment status, Suicide Ideation, recent cannabis use, 
        # recent heroin use, life time cocaine use, lifetime amphetamine use (p. 134)

  
  m_pre = c(
    9.5, 12.56, # Beck Scale for Suicide Ideation (range 0–38)
    9.3, 9.89,  # HAD Scale Depression (range 0–21)
    12.58, 12.35, # HAD Scale Anxiety (range 0–21)
    61.76, 65.09  # Self-Efficacy Scale (range 0–90)
  ),
  
  sd_pre = c(
    9.61, 8.55,
    6.66, 5.6, 
    6.66, 5.39,
    15.34, 12.55
  ),
  
  m_post = c(
    5.83, 6,
    6.4, 7.1,
    8.83, 9.71,
    65.31, 65.13
  ),
  
  sd_post = c(
    5.58, 6.61,
    5.43, 4.3,
    5.72, 4.3,
    12.49, 9.58
  )
  
  ); Morley2014



# Turning dataset into wide format
Morley2014_wide <-
  Morley2014 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", 
    "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


# Effect size calculating 
Morley2014_est <-           
  Morley2014_wide |>
  mutate(
    
     analysis_plan = case_when(
      str_detect(outcome, "BSS") ~ "All mental health outcomes",
      str_detect(outcome, "HADS_depression") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "HADS_anxiety") ~ "All mental health outcomes/Anxiety",
      str_detect(outcome, "SES") ~ "Hope, Empowerment & Self-efficacy",
      .default = NA_character_
    ),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
 
    study = "Morley et al. 2014",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # Reverting m_post for specific outcomes
    m_post = if_else(outcome %in% c("BSS", "HADS_depression", "HADs_anxiety"),
            (m_post_t - m_post_c) * -1, 
            m_post_t - m_post_c),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    
    # Reverting specific values
   m_diff_t = if_else(outcome %in% c("BSS", 
                                     "HADS_depression", 
                                     "HADs_anxiety"),
                       (m_post_t - m_pre_t)*-1,
                        m_post_t - m_pre_t),
    m_diff_c = if_else(outcome %in% c("BSS", 
                                     "HADS_depression", 
                                     "HADs_anxiety"),
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
  
    # Group sizes was between 6 to 8
    avg_cl_size = 7, 
    avg_cl_type = "From study (p. 132)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    )
    
  ) |> 
  ungroup() |> 
  mutate(
    vary_id = outcome
  ); Morley2014_est 

```

```{r, echo=FALSE}
# Adding Morley to data
dat <- 
  dat |> 
  bind_rows(Morley2014_est)
```

### **Morton et al. [-@Morton2012] (Quality-checked)**

Entering data from Table 3 (p. 538)

```{r}
# Data from table 3 containing means and standard deviation (p. 538)
Morton2012 <- tibble(
  group = as.factor(rep(c("Intervention", 
                          "Control"), 
                        each = 1,4)),
  
  outcome = as.factor(rep(c("BEST_composite",
  #                          "BEST_BPD_thoughts_&_feelings", The subscaled are removed 
  #                                                          we only use total-scale
  #                          "BEST_BPD_negative_behaviors",
  #                          "BEST_BPD_positive_behaviors",
  #                          "DASS_stress",
                            "DASS_anxiety",
  #                          "DASS_depression",
                            "BHS",
 #                            "AAQ-II",
                            "DERS_total"
 #                           "FFMQ_total",
 #                            "ACS_total"
  ),
  each = 2,1)),
  
  N = rep(c(21, 20),
          each = 1,4),
 
  
  m_pre = c(
    44.57, 49.80, # BEST composite
#     28.48, 30.3, # DASS stress
     23.33, 24.20,# DASS anxiety
#    31.52, 33.7, # DASS depression
    14.40, 15.7, # BHS
    # 24.10, 22.55,# AAQ-II
     131.76, 134.42 # DERS total
    # 96.52, 92.15,# FFMQ total
    # 4.86, 4.93   # ACS total
  ),
  
  sd_pre = c(
    11.16, 12.35,
 #   10.04, 10.16,
    9.34, 11.76,
 #    10.86, 8.74,
    4.87, 3.58,
    # 9.29, 8.32,
     25.15, 19.45
    # 23.01, 20.81,
    # 0.74, 0.68
  ),
  
  m_post = c(
    32.76, 47.42,
 #   26.55, 31.57,
    19.67, 26.28,
#    22.67, 31.00,
    9.7, 16.43,
  #  35.3, 23.1,
    113.04, 140.04
  #  108.81, 90.87,
  #  4.35, 5.08
  ), 
  
  sd_post = c(
    12.47, 11.00,
 #   11.58, 9.93,
    11.02, 8.33,
#    14.73, 8.51,
    6.34, 3.69,
  #  10.8, 7.1,
    17.64, 20.88
  #  19.11, 20.67,
  #  0.65, 0.56
  )

); Morton2012


Morton2012_wide <-
  Morton2012 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


Morton2012_wide2 <- tibble(
  outcome = c("DASS_stress", 
              "DASS_depression"),
  
  N_t = c(21),
  N_c = c(20),
  
  m_pre_t = c(
    28.48,
    31.52
  ), 
  
  m_pre_c = c(
    30.3, 
    33.70
  ), 
  
  sd_pre_t = c(
    10.04,
    10.86
  ),
  
  sd_pre_c = c(
    10.16,
    8.74
  ),
  
  m_post_t = c(
    26.55,
    22.67
  ),
  
  m_post_c = c(
    31.57,
    31
  ),
  
  sd_post_t = c(
    11.58,
    14.73
  ),
  
  sd_post_c = c(
    9.93, 
    8.51
  )
  
)



Morton2012_est <- 
  bind_rows(
    Morton2012_wide,
    Morton2012_wide2
  ) |> 
  mutate(
    study = "Morton et al. 2012",
   
     analysis_plan = case_when(
      str_detect(outcome, "BEST|DASS") ~ "All mental health outcomes",
      str_detect(outcome, "BHS") ~ "Hope, Empowerment & Self-efficacy",
      str_detect(outcome, "DERS") ~ "All mental health outcomes/Unused outcomes",
      .default = NA_character_
    ),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
  # Entering test-retest reliability measure from text and 
  # Pfohl, B., Blum, N., St. John, D., McCormick, B., Allen, J., & Black, D.W.
  #   (2009). Reliability and validity of the Borderline Evaluation of
  #   Severity over Time (BEST): A self-rated scale tomeasure severity and
  #   change in persons with Borderline Personality Disorder. Journal of
  #   Personality Disorders, 23, 281–293.  

  ppcor = case_when(
    str_detect(outcome, "BEST") ~ 0.62,
    str_detect(outcome, "DASS") ~ 0.81,
    str_detect(outcome, "BHS") ~ 0.69,
    str_detect(outcome, "DERS") ~ 0.88,
    .default = ppcor_imp
    
  ),
  ppcor_method = "Obtained from test-retest reliability measures"
    
  )




Morton2012_est <- 
  Morton2012_est|> 
  mutate(
    N_start_t = N_t,
    N_start_c = N_c,
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # reverting all scores as lower score is beneficial
    m_post =  (m_post_t - m_post_c) * -1,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    #  reverting all scores as lower score is beneficial
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
  
    # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Morton2012_est

  


# Regression coefficients extracted from text (pp.537-539)
# Not used for analysis!
morton2012_es <- tibble(
  
  study = "morton2012",
  
  es_method = "Multilevel model",
  
  group = rep(c("treatment", # Treatment is ACT + TAU
                "control", # Control is TAU.
                "interaction"), each = 1,4),
  
  outcome = rep(c(
                  "BEST_composite", 
#                  "BEST_BPD_thoughts_feelings", 
#                  "BEST_BPD_negative_behaviors", 
                  "DASS_anxiety", 
                  "BHS", 
#                  "AAQ-II", 
                  "DERS_total" 
#                  "FFMQ_total",
#                  "ACS_total"
                  ), each = 3,1),
  
  beta = c(-11.52, -1.8, 9.71,  # BEST Composite
#           -6.6, -0.05, 6.54, # BEST BPD thoughts and feelings
#           -1.11, -3.98, 2.87, # BEST BPD negative and behaviors
           -3.92, 3.98, 7.90, # DASS anxiety
           -4.93, 0.62, 5.55, # BHS
#           5.55, 0.37, -9.88, # AAQ-II
           -19.17, 4.77, 23.94 # DERS total
#           12.30, -0.31, -12.62, # FFMQ total
#           -0.54, 0.18, 0.71), # ACS total
),
  
  se = c(2.75, 3.19, 4.21, 
#         1.77, 2.05, 2.72, 
#         1.04, 0.90, 1.38,
         2.24, 2.50, 3.36,
         1.27, 1.38, 1.87,
#         1.87, 2.65, 3.60,
         5.68, 6.30, 8.48
#         3.71, 4.06, 5.50,
#         0.15, 0.16, 0.21),
),
  
  t = c(2.3, -0.57, 2.3, 
#        -3.72, -0.03, 2.41, 
#        -1.06, -4.42, 2.08,
        -1.74, 1.59, 2.35,
        -3.88, 0.45, 2.96,
#        2.96, 0.139, -2.74,
        -3.38, 0.76, 2.82
#        3.31, -0.08, -2.29,
#        -3.63, 1.15, 3.33), # unsure about this
),
  
  p = c(0.000, 0.575, 0.28, 
#        0.001, 0.979, 0.022, 
#        0.296, 0.0, 0.046,
        0.091, 0.121, 0.025,
        0.000, 0.656, 0.006,
#        0.006, 0.891, 0.010,
        0.002, 0.454, 0.008
#        0.002, 0.939, 0.028,
#        0.001, 0.258, 0.002),
),
  
  d = c(0.99,0.15, 0.81, 
#        0.88, 0.01, 0.85, 
#        1.05, 0.29, 0.75,
        0.41, 0.41, 0.83,
        0.91, 0.11, 1.02,
#        1.02, 0.04, 0.99,
        0.78, 0.19, 0.98
#        0.79, 0.02, 0.80,
#        0.89, 0.30, 1.20)
)
  
)
```

```{r, echo=FALSE}
# Adding Mortonto data
dat <- 
  dat |> 
  bind_rows(Morton2012_est) 
```

### **Patterson et al. [-@Patterson2003] (Quality-checked)**

Entering data from Table 3 (p. 21)

```{r Patterson et al 2003}
# Data from Table 3 (p. 21)  containing means, standard deviation. It also contains
# Time X Interaction coefficients with p-values (estimated with ANOVA) 

Patterson2003 <- 
  tibble(
    group = as.factor(rep(c("FAST Intervention", 
                            "Treatment-as-Usual"), 
                          each = 1, 10)),
    
    timing = rep(c("3m", "6m"), each = 10,1),
    
    outcome = as.factor(rep(c(
      #    "UPSA", 
      "PANSS_Positive", 
      "PANSS_Negative", 
      "PANSS_General", 
      "HAM_D", 
      "QWB"), each = 2, 2)),
    
    N = 16,
    
    N_start = 16,
    
    m_pre = rep(c(
      #    31.9, 41.5,   # UPSA
      12.5, 10.4,   # PANSS Positive
      16.9, 10.1,   # PANSS Negative
      25.0, 22.3,   # PANSS General
      7.8, 4.6,     # Ham-D
      0.53, 0.49    # QWB
    ), each = 1,2),
    
    sd_pre = rep(c(
      #    11.8, 8.3,    # UPSA
      5.6, 4.0,       # PANSS Positive
      6.6, 3.2,     # PANSS Negative
      6.2, 3.7,     # PANSS General
      6.1, 2.8,     # Ham-D
      0.08, 0.08    # QWB
    ), each = 1,2),
    
    
    m_post = c(
      #    41.5, 40.3, # UPSA           3 months
      13.0, 14.0, # PANSS Positive 3 months
      13.0, 10.9, # PANSS Negative 3 months
      21.9, 21.5, # PANSS General  3 months
      7.7, 8.0,   # Ham-D          3 months
      0.55, 0.5, # QWB            3 months
      
      #    42.7, 41.6,  # UPSA           6 months
      12.13, 13.1, # PANSS Positive 6 months 
      14.3, 12.4,  # PANSS Negative 6 months
      23.9, 23.9,  # PANSS General  6 months
      7.2, 7.9,    # Ham-D          6 months
      0.51, 0.49   # QWB            6 months
      
    ),
    
    sd_post = c(
      #    9.5, 7.1,     # UPSA           3 months
      6.6, 6.3,     # PANSS Positive 3 months
      5.1, 4.0,       # PANSS Negative 3 months
      4.7, 4.9,     # PANSS General  3 months
      5.2, 5.3,     # Ham-D          3 months
      0.10, 0.07,   # QWB           3 months
      
      
      
      #    9.7, 9.7,     # UPSA           6 months
      4.3, 5.9,     # PANSS Positive 6 months
      6, 4.4,       # PANSS Negative 6 months
      4.6, 4.4,     # PANSS General  6 months
      4.9, 5.0,       # Ham-D          6 months
      0.07, 0.07    # QWB            6 months
    )
  ) 


# Making the patterson2003 tibble wide in order to estimate the effect sizes and
# further analysis. 

Patterson2003_wide  <- 
  Patterson2003 |> 
  mutate(group = case_match(group, "FAST Intervention" ~ "t", "Treatment-as-Usual" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )   

Patterson2003_est <- 
  Patterson2003_wide |>
  # Based on the degrees of freedom value reported in Druss et al. 2018 table 2 and 3
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "PANSS_General|PANSS_Negative|PANSS_Positive") ~ "All mental health outcomes/Symptoms of psychosis",
      str_detect(outcome, "HAM_D") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "QWB") ~ "Wellbeing and Quality of Life",
      .default = NA_character_
    ),
    
    study = "Patterson et al. 2003",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    
  ) |> 
  
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    # For PANSS and HAM-D lower scores are beneficial why these is reverted
    m_post = if_else(analysis_plan %in% c("All mental health outcomes/Symptoms of psychosis",
                                          "All mental health outcomes/Depression"), 
                     (m_post_t - m_post_c)*-1,
                     m_post_t - m_post_c),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    
    #  reverting specific scores as lower score is beneficial
    m_diff_t = if_else(analysis_plan %in% c("All mental health outcomes/Symptoms of psychosis",
                                            "All mental health outcomes/Depression"), 
                       (m_post_t - m_pre_t)*-1,
                       m_post_t - m_pre_t),
    m_diff_c = if_else(analysis_plan %in% c("All mental health outcomes/Symptoms of psychosis",
                                            "All mental health outcomes/Depression"),
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
    
  ) |> 
  rowwise() |>
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    )
    
  ) |> 
  ungroup() |> 
  mutate(
    vary_id = paste0(outcome, "/", timing)
  ); Patterson2003_est
```

```{r, echo=FALSE}
# Adding Patterson to data
dat <- 
  dat |> 
  bind_rows(Patterson2003_est) 
```

### **Popolo et al. [-@Popolo2019] (Quality-checked)**

Entering data from Table 2 (p. 351)

```{r Popolo et al 2019}
# Data from Table 2 containing means and standard deviation + within effect size.
# Table also contains "between group-group post treatment effect size at post-tratement"
# with a coefficient and "Post hoc power" on page 351

Popolo2019 <- 
  tibble(
    group = rep(c("Intervention", 
                  "Control"), 
                each = 1,3),
    
    outcome = rep(c("CORE_OM",
                    "DERS",
                    "TAS"
                   #"Metacognition_self",
                   #"Metacognition_others",
                   #"Metacognition_decentration",
                   #"Metacognition_mastery"
    ), each = 2,1),
    
    
    N = rep(c(8, 10), 3),
    
    N_start = 10, 
    
    m_pre = c(
      12.85, 15.24, # CORE-OM Total
      95, 100.9, # DERS Total
      54.2, 51.5 # TAS Total
      #    4.3, 4.1, # Metacognition Self
      #    2.1, 2.85, # Metacognition Others
      #    0.75, 1.15, # Metacognition Decentration 
      #    2.9, 3.3    # Metacognition Mastery
    ),
    
    sd_pre = c(
      4.6, 6.68,
      16.19, 17.67,
      11.52, 10.99
      #    1.32, 0.97,
      #    0.52, 1.31,
      #    0.75, 1.03,
      #    1.07, 0.67
    ),
    
    m_post = c(
      7.68, 11.78,
      76.4, 97.11,
      43.5, 52.63
      #    5.75, 3.35,
      #    2.9, 3.3,
      #    1.15, 0.7,
      #    4.18, 3.05
    ),
    
    sd_post = c(
      1.94, 4.59,
      26.78, 29.17,
      11.7, 12.59
      #    1.36, 1.27,
      #    1.07, 0.67,
      #    1.03, 0.48,
      #    1.35, 1.23
    ), 
    
    # Within-group effect size baseline – 3-month follow-up
    within_group_d = c( 
      1.14, 0.64,
      0.74, 0.16,
      1.11, 0.10
      #    1.73, 0.56,
      #    0.99, 0.32,
      #    0.59, 0.51,
      #    1.51, 0.20
    ),
    
    # To calculated the pre-posttest correlation, we need the standard deviation of
    # the mean differences (sd_diff). As can be seen from page 351. We can obtain
    # sd_diff from the reported within-group effect sizes as follows
    sd_diff = abs(m_post - m_pre)/within_group_d,
    
    
    # The group individual pre-posttest correlation can be obtained as follows.
    # Note that we are not able to obtained correlations from d_rm = 0. This only 
    # concerns SCL90 pre-posttest effect sizes for the control group. Hereto, 
    # 
    r = (sd_pre^2 + sd_post^2 - sd_diff^2)/(2*sd_pre*sd_post),
    
    # To estiamte the average pre-posttest correlation
    z = 0.5 * log( (1+r)/(1-r) ),
    v = 1/(N-3),
    w = 1/v
    
  ); Popolo2019


Popolo2019_wide <-
  Popolo2019 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


Popolo2019_est <- 
  Popolo2019_wide |>
  mutate(
    
    analysis_plan =
      c("All mental health outcomes", 
        "All mental health outcomes/Unused outcomes", 
        "All mental health outcomes"),
    
    study = "Popolo et al. 2019",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    mean_z =  (w_t*z_t + w_c*z_c)/(w_t + w_c),  
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "Calculated from study results",
    
    )|> 
  
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # reverting all scores as lower score is beneficial
    m_post =  (m_post_t - m_post_c) * -1,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
    
  ) |>
  rowwise() |> 
  mutate(
    
    # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc, sqrt = TRUE
    ),
    
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    vary_id = outcome
    
  ) |> 
  ungroup() |> 
  rename(
    d_rm_t = within_group_d_t,
    d_rm_c = within_group_d_c
    
  ); Popolo2019_est

```

```{r, echo=FALSE}
# Adding Popolo to data
dat <- 
  dat |> 
  bind_rows(Popolo2019_est)
```

### **Rabenstein et al. [-@Rabenstein2015] (Quality-checked)**

Data was extracted from Table 2 (p.6)

```{r Rabenstein et al 2015}
# Based on Table 2, p. 6
Rabenstein2015 <- 
  tibble(
    group = rep(c("Intervention", "Control"), each = 1, 3),
    
    outcome = rep(c(
      #  "Somatization", 
      #  "OCD", 
      #  "Insecurity", 
      #  "Depression",
      #  "Anxiety",
      #  "Aggressiveness",
      #  "Phobic Anxiety",
      #  "Paranoid Thinking",
      #  "Psychoticism",
      "BSI (GSI)", 
      "BDI", 
      "WHOQOL"
      #  "Physical",
      #  "Mental", 
      #  "Social Relations", 
      #  "Environment"
    ), each = 2,1),
    
    
    
    N = rep(c(
      153, 148
    ), each = 1, 3),
    
    
    N_start = rep(c(
      170
    )),
    
    m_pre = c(
      #    8.11, 8.08,
      #    9.85, 12.04,
      #    5.58, 7.23,
      #    9.22, 10.45,
      #    7.82, 9.91,
      #    4.08, 5.45,
      #    5.15, 6.43,
      #    5.83, 7.24,
      #    4.74, 5.76,
      1.23, 1.48,
      23.39, 25.04,
      40.71, 42.87
      #    50.78, 50.35,
      #    38.52, 37.9,
      #    53.31, 47.49,
      #    61.68, 63
    ),
    
    sd_pre = c(
      #    5.51, 5.79,
      #    5.45, 5.30,
      #    4.04, 3.67,
      #    6.08, 5.28,
      #    5.09, 5.20,
      #    3.61, 3.23,
      #    4.92, 4.83,
      #    4.35, 4.09,
      #    3.72, 3.87,
      0.68, 0.66,
      10.92, 9.83,
      22.38, 21.37
      #    19.56, 15.96,
      #    19.55, 16.43,
      #    24.22, 23.85,
      #    18.62, 16.04
    ),
    
    m_post = c(
      #    4.49, 7.15,
      #    7.02, 10.25,
      #    4.06, 6.63,
      #    5.68, 9.87,
      #    4.45, 8.59,
      #    2.74, 4.15,
      #    2.91, 5.59,
      #    4.82, 6.25,
      #    3.12, 4.93,
      0.81, 1.30,
      15.24, 22.08,
      53.21, 46.1
      #    59.43, 52.07,
      #    48.45, 38.49,
      #    58.23, 50.27,
      #    65.83, 65.83
    ),
    
    sd_post = c(
      #    4.46, 5.64,
      #    5.16, 5.63,
      #    3.53, 4.06,
      #    5.18, 5.63,
      #    4.36, 5.40,
      #    2.45, 3.18,
      #    3.67, 4.62,
      #    4.26, 4.51,
      #    3.25, 3.91,
      0.60, 0.67,
      10.78, 9.95,
      22.83, 20.44
      #    21.37, 15.32,
      #    19.94, 17.32,
      #    23.77, 22.13,
      #    18.08, 17.12
    ),
    
    tval_paired = c(
      7.355,3.428,
      8.377, 4.018,
      -6.695, -1.865
    ),
    
    r = ((sd_pre^2*tval_paired^2 + sd_post^2*tval_paired^2) - 
           (m_post-m_pre)^2 * N)/ (2 * sd_pre*sd_post*tval_paired^2),
    
    
    z = 0.5 * log( (1+r)/(1-r) ),
    v = 1/(N-3),
    w = 1/v
    
    
  ); Rabenstein2015

# Making it a wide format:

Rabenstein2015_wide <-
  Rabenstein2015 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


# Effect size calculation:

Rabenstein2015_est <-           
  Rabenstein2015_wide |>
  mutate(
    analysis_plan =
      c("All mental health outcomes", 
        "All mental health outcomes/Depression",
        "Wellbeing and Quality of Life"),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
   
    study = "Rabenstein et al. 2015",
    
    # There are also some effect sizes in the raw text on p. 7
    # the time X group effect: ANOVA
    #  df1 = c(1), 
    #  df2 = c(138, 187, 169),
    #  F_val = c(25.584, 20.582, 13.252),
    # p_val_F = c(0.001, 0.001, 0.001),
    #  eta_sqrt = c(0.156, 0.002, 0.0073),
    
    # t-test
    # t_val = c(-4.468, -4.531,2.146),
    # p_val_t = c(0.001, 0.001, 0.012),
    #  d = c(0.76, 0.66, 0.55)
    
    main_es_method = "Raw diff-in-diffs",
    
    mean_z =  (w_t*z_t + w_c*z_c)/(w_t + w_c),  
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "Calculated from study results, i.e., paired t-tests",
    
  ) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # The outcome BSI and BDI are reverted because lower score is beneficial
    m_post = if_else(outcome %in% c("GSI","BDI"), 
                     (m_post_c - m_post_t)*-1, # Revert the difference if BSI or BDI
                     m_post_t - m_post_c),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # The outcome BSI and BDI are reverted because lower score is beneficial  
    m_diff_t = if_else(analysis_plan %in% c("GSI","BDI"),
                       (m_post_t - m_pre_t)*-1, m_post_t - m_pre_t),
    m_diff_c = if_else(analysis_plan %in% c("GSI","BDI"),
                       (m_post_c - m_pre_c)*-1,m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc, sqrt = TRUE
    ),
    
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size,
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = outcome
  ) |> 
  ungroup(); Rabenstein2015_est
```

```{r, echo=FALSE}
# Adding Rebaenstein to data
dat <- 
  dat |> 
  bind_rows(Rabenstein2015_est) 
```

### **Rosenblum et al. [-@Rosenblum2014] (Quality-checked)**

Entering data from Table 3 (p. 85)

```{r Rosenblum et al 2014}
# Extracting data from table 3 p. 85 

Rosenblum2014 <- tibble(
  
  group = as.factor(rep(c(
    "Double Trouble in Recovery (DTR)",
    "waiting list control group"), 
    each = 1,5
  )),
  
  # sample = rep(c(
  #    "Total"
  #  "New York",
  #  "Michigan"
  #  ), each = 10,1),
  
  
  outcome = rep(c(
    "Days any alcohol past 30",
    "Days heavy alcohol past 30",
    "Days any drugs past 30",
    "Days any drugs or alcohol past 30",
    
    "Medication Adherence"
    
   # "RQOL (quality of life)c (total scale)" Only measured at follow-up 
   ), each = 2
 ),
 
 N = c(
   rep(c(91,70), each = 1,5) # Total 
   
   #rep(c(35, 32), each = 1,5), # New York
   
   #rep(c(56, 38), each = 1,5) # Michigan
   
 ),
 
 N_start = c(
   rep(c(113,90), each = 1,5) # Total 
   
  # rep(c(50, 40), each = 1,5), # New York
   
  # rep(c(50), each = 1,5) # Michigan
   
 ),
 
 m_pre = c(
   4.5, 6.5,
   2.6, 2.1,
   6.9, 7.7,
   9.3, 11.9,
   1.5, 1.5

   
 #  1.9, 3.9,
 #  0.3, 0.8,
 #  4.2, 7.1,
 #  4.7, 9.9,
 # 1.4, 1.4,

   
 #  6.1, 8.6, 
 # 4.0, 3.1,
 #  8.6, 8.2,
 # 12.2, 13.6,
 # 1.6, 1.6 

 ),
 
 sd_pre = c(
   8.2, 9.6,
   6.1, 5.1,
   9.3, 10.4,
   10.1, 11.2,
   0.5, 0.5

   
#   4.7, 7.2,
#   1.0, 1.8,
#   6.3, 10.2,
#   6.1, 10.8,
#   0.4, 0.4,
 
   
#   9.5, 11.0,
#   7.4, 6.5,
#   10.5, 10.6, 
#   11.0, 11.5,
#   0.5, 0.5
  
 ), 
 
 m_post = c(
   3.1, 6.1,
   1.1, 2.1, 
   5.5, 8.0,
   6.8, 11.3,
   1.4, 1.4
#   2.7, 2.0,
   
 #  1.6, 3.2,
#   0.3, 1.0, 
#   3.4, 8.0,
#   4.5, 10.2,
#   1.4, 1.3,
#   3.5, 3.2,
   
 #  4.0, 8.4,
#   1.5, 3.0, 
#   6.8, 8.0,
#   8.3, 12.2,
#   1.5, 1.6
#   2.1, 1.0
 ),

sd_post = c(
  6.4, 8.8,
  2.8, 6.0,
  9.2, 10.6,
  9.8, 11.1,
  0.4, 0.4
  # 1.8, 1.8,
  
#  4.4, 7.2,
#  1.3, 4.5,
 # 7.1, 10.2, 
#  7.8, 10.8, 
#  0.3, 0.3,
  #  1.5, 1.7,
  
#  7.3, 9.5, 
#  3.4, 7.0,
#  10.2, 11.1,
#  10.6, 11.4, 
#  0.5, 0.4
# 1.8, 1.3
),

#q = c(
#  rep(c(1), each = 1,10), # The baseline equivalent of the dependent variable was used as a covariate, p. 85
#  rep(c(2), each = 1, 4), # The baseline variable PTSD diagnosis was added as a covariate to these analyses because it was correlated with study condition and the #outcome variable, p. 85
#  rep(c(1), each  = 1,2),
#  rep(c(2), each = 1,2),
#  rep(c(1), each = 1,12)
#  )
); Rosenblum2014



Rosenblum2014_wide <-
  Rosenblum2014 |> 
  mutate (group = case_match(
    group, "Double Trouble in Recovery (DTR)" ~ "t", "waiting list control group" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


Rosenblum2014_est <- 
  Rosenblum2014_wide |> 
  mutate(
    
  #  p_val = c(0.03, 0.11, 0.07, 0.02, 0.45, 
  #            0.84, 0.47, 0.07, 0.17, 0.73,
  #            0.01, 0.10, 0.31, 0.06, 0.15),
    

    analysis_plan = case_when(
      str_detect(outcome, "Days") ~ "Alcohol and drug abuse/misuse",
      str_detect(outcome, "Medication Adherence") ~ "Unused outcomes",
      .default = NA_character_
    ),
    
    study = "Rosenblum et al. 2014",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    
) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # all scores are reverted because lower scores is beneficial 
    m_post =  (m_post_t - m_post_c)*-1,
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # all scores are reverted because lower scores is beneficial 
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
       # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    vary_id = paste0(outcome)
  ) |> 
  ungroup(); Rosenblum2014_est
```

```{r, echo=FALSE}
# Adding Rosenblum to data
dat <- 
  dat |> 
  bind_rows(Rosenblum2014_est)
```

### **Russinova et al. [-@Russinova2018] (Quality-checked)**

Entering data from Table 4 (p. 203)

```{r Russinova et al 2018}
# Taken from table 
Russinova2018 <- tibble(
  group = rep(c("VEP", "Control group"),
              each = 1,10),
  
  
  outcome = rep(c("Empowerment scale", 
                  "ISMI Scale", 
                  "Work Hope Scale",
                  "Vocational identity", 
                  "Work motivation"), 
                each = 2, 2),

  timing = rep(c(
    #" 10 weeks post-baseline", 
                 "Post", 
                 "3m"), each = 10),
  
  
  N = c(
    # 10 weeks post-base
#    23, 25,
#    23, 25,
#    23, 25,
#    23, 24,
#    21, 23, 
  
  # Post intervention
    22, 26,
    22, 26,
    22, 26,
    22, 26,
    21, 24,
  
  # 3m followup
    22, 25,
    22, 25,
    22, 25,
    22, 25,
    21, 24),

  N_start = rep(c(
    24, 27,
    24, 26,
    24, 27,
    24, 27,
    21, 23
  ), each = 1,2),
  
  m_pre = rep(c(
    2.86, 2.84,
    2.11, 2.16, 
    4.38, 4.16,
    7.79, 7.04,
    3.09, 2.96),
    each = 1,2), 
  
  sd_pre = rep(c(
    .3, .21,
    .5, .43,
    1.13, .79,
    5.02, 5.31,
    .59, .47),
    each = 1,2),
  
  m_post = c(
    # 10 weeks post-base
  #  3.02, 2.85,
  #  1.93, 2.17, 
  #  4.63, 4.26,
  #  8.61, 6.88,
  #  3.11, 2.97,
  
    # Post intervention
    2.98, 2.92,
    1.86, 2.23,
    4.64, 4.29,
    9.09, 6.77,
    3.15, 3.09,
    
    # 3m followup
    2.96, 2.88,
    1.92, 2.08,
    4.67, 4.58,
    9.23, 8.32,
    3.17, 3.04
    ),
  
  sd_post = c(
    # 10 weeks post-base
  #  .34, .21,
  #  .43, .44,
  #  1.21, .96,
  #  4.49, 4.48,
  #  .67, .49,
    
    # Post intervention
    .34, .24,
    .44, .41,
    1.29, 1.15,
    5.08, 4.99,
    .58, .55,
    
    # 3m followup
    .34, .29,
    .5, .43,
    1.24, .94,
    5.05, 5.17,
    .6, .5) 
  
  ); Russinova2018

# Turning to wideformat
Russinova2018_wide <- Russinova2018 |> 
      mutate(group = case_match(group, "VEP" ~ "t", "Control group" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
      )

Russinova2018_est <- Russinova2018_wide |> 
  mutate(
    # Group effect is estimated across time scores.
    # df1 = c(
    #  rep(c(NA_real_), each = 5,1)
    #,
    #  rep(c(1), each = 5,1)),
    
    # df2 = c(
    #  rep(c(NA_real_), each = 5,1)
    # ,
    #  c(91,
    #    90,
    #    92,
    #    91,
    #    75)),
    
  #  F_val = c(
  #    rep(c(NA_real_), each = 5,1),
      
  #    6.65,
  #    5.84,
  #    1.39,
  #    1.98,
  #    0.08    
  #  ),
  #  p_val_F = c(
      
  #     rep(c(NA_real_), each = 5,1),
      
  #    0.01,
  #    0.02,
  #    0.24,
  #    0.16,
  #    0.78
  #  ),
    
  #  d = c(
  #     rep(c(NA_real_), each = 5,1),
       
  #     0.39,
  #     -0.65,
  #     0.32,
  #     0.41,
  #     0.30
  #  ),
    study = "Russinova et al. 2018",
    
    analysis_plan = case_when(
      str_detect(outcome, "Empowerment|Work Hope|Work motivation") ~ "Hope, Empowerment & Self-efficacy",
      str_detect(outcome, "ISMI Scale") ~ "Self-esteem",
      str_detect(outcome, "Vocational identity") ~ "Employment",
      .default = NA_character_
    ),
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    
  ) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # 
    m_post = if_else(!outcome %in% c("ISMI Scale"), m_post_t - m_post_c,
                     (m_post_t - m_post_c)*-1),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # The outcome BSI and BDI are reverted because lower score is beneficial  
    m_diff_t = if_else(!outcome %in% c("ISMI Scale"),
                        m_post_t - m_pre_t,(m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(!outcome %in% c("ISMI Scale"),
                       m_post_c - m_pre_c, (m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    ) |> 
  
  rowwise() |> 
  
  mutate(
      # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ), 
    
    vary_id = paste0(outcome, "/", timing)
  ) |> 
    ungroup(); Russinova2018_est
```

```{r, echo=FALSE}
# Adding Russinova to data
dat <- 
  dat |> 
  bind_rows(Russinova2018_est) 
```

### **Rüsch et al. [-@Rüsch2019] (Quality-checked)**

Data was extracted from Table 1 (p. 35). NOTE: We suspect that the reported F values from ANCOVA for Job-search and Help-seeking at Time-3 (T3) are prone to error. Since they seem to be too small compared with the reported change score differences. Thus, we did not use these values for sampling variance estimation of the corresponding effect sizes.

```{r}
# Data from Table 1 containing means and standard deviation on page 335
# The table also contains partial η2 values, p-values and F-test

Rüsch2019 <- 
  tibble(
    group = rep(c("Intervention", 
                  "Control"), 
                each = 1,12),
    
    timing = rep(c("6w", 
                   "6 months"), 
                 each = 12,1),
    
    outcome = rep(c("Job_search", 
                    "Help_seeking", 
                    "SISR",
                    "SSMIS_SF",
                    "CES-D", 
                    "BHS"  
                    #  "Secrecy"
    ), 
    each = 2,2 ),
    
    N = c(rep(c(
      18, 17), # 6 weeks 
      times = 6), 
      rep(c(
        
        20, 13), # 12 months
        times = 6)),
    
    N_start = rep(c(
      23, 19
    ), each = 1,12),
    
    m_pre = rep(c(
      3.1, 3.3, # Job-search self-efficacy (1–5)
      3.4, 3.0,  # Help-seeking intentions (1–7)
      15.3, 16.3, # Recovery (SISR: 4–24)
      17.3, 17.3,  # Self-stigma (SSMIS-SF: 5–45)
      39.2, 41.4, # Depressive symptoms (CES-D: 15–60)
      14.9, 14.1 # Hopelessness (BHS: 4–24)
      #    4.0, 3.7   # Link’s 5-item Secrecy Scale
    ), each = 1,2),
    
    sd_pre = rep(c(
      0.8, 0.8,
      1, 1,
      4.1, 4.2,
      4.3, 8.1,
      7.4, 7.2,
      3.6, 4.5 
      #    1.2, 1.6
    ), each = 1,2),
    
    m_post = c(
      # 3 Weeks
      #  3.2, 3.2, 
      #  3.6, 3,
      #  16.1, 15.9, 
      #  15.3, 16.9, 
      #  35.2, 39.1,
      #  13.8, 13.7,
      #  3.8, 3.7
      
      # 6 weeks
      3.4, 3.3, 
      3.5, 3.2, 
      17.8, 16.1, 
      14.6, 19.1, 
      31.5, 40.1, 
      12.6, 14.2, 
      #    3.5, 3.9,
      
      # 6 months 
      3.2, 3.2,
      3.3, 3.1,
      17.3, 15.7,
      15.4, 18.3,
      32, 37.5,
      12.3, 14.4
      #    3.7, 4.4
      
    ),
    
    
    sd_post = c(
      # 3 weeks
      # 0.7, 0.9, 
      # 1, 0.9, 
      # 4.2, 3.6, 
      # 6.2, 8, 
      # 8.8, 10.2, 
      # 3.9, 4.6, 
      # 1, 1.7
      
      # 6 weeks
      0.8, 0.9,
      1, 1, 
      3.4, 4.4,
      7.9, 8.7,
      8.5, 10.1,
      3.6, 5, 
      #  1.3, 1.7,
      
      # 6 months
      1, 0.9, 
      0.6, 0.9,
      3.5, 2.6,
      7, 9.6,
      7.6, 9.8,
      4.2, 3
      #  0.9, 1.4
    )
  ); Rüsch2019



# Turning data into wide format

Rüsch2019_wide <- Rüsch2019 |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N:last_col()
  )


Rüsch2019_est <- 
  Rüsch2019_wide |> 
  mutate(
    
    analysis_plan = case_when(
      str_detect(outcome, "CES-D") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "Job_search|Help_seeking|BHS|SISR") ~ "Hope, Empowerment & Self-efficacy",
      str_detect(outcome, "SSMIS_SF") ~ "Self-esteem",
      .default = NA_character_
    ),
    
    # ANCOVA estimates taken from table 1, p. 335
    F_val = c(
      # 12 weeks
      1.4, 0.2, 4.6, 10.8, 6.3, 3.4,
      # 6 months
      0.01, 0.04, 1.9, 1.5, 0.9, 2.4
    ),
    
    #eta_sqrt = c(
    # 12 weeks
    # 0.04, 0.007, 0.13, 0.25, 0.17, 0.10,
    
    # 6 months
    #  0.01, 0.01, 0.06, 0.05, 0.03, 0.07
    #),
    
    #p_val_f = c(
    # 12 weeks
    # 0.24, 0.64, 0.039, 0.08, 0.17, 0.08,
    
    # 6 months
    #  0.96, 0.85, 0.17, 0.23, 0.35, 0.14
    
    
    
    study = "Rüsch et al. 2019"
  ) |> 
 # arrange(outcome, desc(timing)) |> 
  rowwise() |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    ppcor = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      ppcor_imp,
      NA_real_
    ),
    ppcor_method = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      "Imputed - We did not trust the reported F values",
      "F values from ANCOVA was used for variance estimation"
    ),
    
    m_post = case_when(
      outcome %in% c("Job_search", "Help_seeking", "SISR") ~ (m_post_t - m_post_c), # Higher is beneficial
      outcome %in% c("SSMIS_SF", "CES-D", "BHS") ~ (m_post_t - m_post_c) * -1), # Lower is beneficial
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # The outcome BSI and BDI are reverted because lower score is beneficial  
    m_diff_t = case_when(
      outcome %in% c("Job_search", "Help_seeking", "SISR") ~ (m_post_t - m_pre_t), # Higher is beneficial
      outcome %in% c("SSMIS_SF", "CES-D", "BHS") ~ (m_post_t - m_pre_t)*-1 # Lower is beneficial
    ), 
    
    m_diff_c = case_when(
      outcome %in% c("Job_search", "Help_seeking", "SISR") ~ (m_post_c - m_pre_c), # Higher is beneficial
      outcome %in% c("SSMIS_SF", "CES-D", "BHS") ~ (m_post_c - m_pre_c)*-1 # Lower is beneficial
    ), 
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
      d_DD^2/F_val + d_DD^2/(2*df_ind)
    ),
    Wd_DD = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      2*(1-ppcor) * (1/N_t + 1/N_c),
      d_DD^2/F_val
    ),
    
    g_DD = J * d_DD, 
    vg_DD = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
      g_DD^2/F_val + g_DD^2/(2*df_ind)
    ),
    Wg_DD = if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      2*(1-ppcor) * (1/N_t + 1/N_c),
      g_DD^2/F_val
    )
  ) |> 
  rowwise() |> 
  mutate(
    # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    
    
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    
    if_else(
      timing == "6 months" & str_detect(outcome, "Help|Job"),
      VIVECampbell::vgt_smd_1armcluster(
        N_cl_grp = N_t, 
        N_ind_grp = N_c, 
        avg_grp_size = avg_cl_size, 
        ICC = icc, 
        g = gt_DD, 
        model = "DiD",
        cluster_adj = FALSE,
        prepost_cor = ppcor,
        q = n_covariates,
        add_name_to_vars = "_DD",
        vars = -var_term1_DD
      ),
      VIVECampbell::vgt_smd_1armcluster(
        N_cl_grp = N_t, 
        N_ind_grp = N_c, 
        avg_grp_size = avg_cl_size, 
        ICC = icc, 
        g = gt_DD, 
        model = "DiD",
        cluster_adj = FALSE,
        F_val = F_val,
        q = n_covariates,
        add_name_to_vars = "_DD",
        vars = -var_term1_DD
      )
    ),
    vary_id = paste0(outcome, "/", timing)
  ) |> 
  ungroup(); Rüsch2019_est
```

```{r, echo=FALSE}
# Adding Rüsch to data
dat <- 
  dat |> 
  bind_rows(Rüsch2019_est) 
```

### **Sacks et al. [-@Sacks2011] (Quality-checked)**

Extracting data from Table 4 (p. 1682)

```{r Sacks et al 2011}
# This study makes estimations based on two different analysis based on their
# propensity scores (what i read as the level of control) - i will keep them
# in the same tibble - "sample" indicates different propensity category. 
#Extracted from table 4 p. 1682 and only means plus standard deviation is taken.

Sacks2011 <- tibble(
  
  group = rep(c("Intervention",
                "Control"), 
              each = 1,10),
  
  
  outcome = rep(c(
#    "alcohol_intoxication",
#    "drug_use",
    "BDI_total",
    "GSI_total",
    "SF-36_mental_health",
    "SF-36_social_functioning",
#    "Health_rating",
    "SF-36_physical_health"
  ), each = 2,2),
  
  
  sample = rep(c("low/medium propensity", 
                 "high propensity"), each = 10,1),
  
  
  
  
  
  N = rep(c(42,34), 
          each = 10,1),
  
  m_pre = c(# Low/medium propensity 
  #          6.8, 6.5, # alcohol
  #          12.9, 17.8, # drugs
            16.1,  18.9, # BDI
            45.3, 47.7, # GSI
            42.4, 37.6, # SF-36 mental
            46.6, 38.5, # SF-36 social functioning 
  #          3.0, 3.0, # Health rating
            44.9, 47.2, # SF-36 physical health
            
            # High propensity
  #          6.3, 7.5, # alcohol
  #          15.9, 8.3, # drugs
            15.3, 15.3, # BDI
            42.0, 41.8, # GSI
            43.5, 43.1, # SF-36 mental
            13.2, 43.6, # SF-36 social functioning
  #          3.0, 3.5, # Health rating
            42.0, 42.6 # SF-36 physical health
            
            ),
  
  
  sd_pre = c(# Low/medium propensity 
  #           2.4, 2.2, # alcohol
  #           7.2, 8.7, # drugs
             6.7, 10.0, # BDI
             4.0, 9.0, # GSI# SF-36 mental
             11.8, 13.0, # SF-36 mental
             11.3, 13.7, # SF-36 social functioning
  #           1.2, 1.1, # Health rating
             9.0, 10.3, # SF-36 physical health
             
             # High propensity
  #           2.3, 0.7, # alcohol
  #           7.5, 5.7, # drugs
             3.7, 11.9, # BDI
             10.3, 7.4, # GSI
             10.4, 9.1, # SF-36 mental
             13.9, 11.3, # SF-36 social functioning
  #           1.0, 0.6, # Health rating
             10.4, 14.0), # SF-36 physical health
  
  
  m_post = c(
    # Low/medium propensity 
  #  2.7, 0.6, # alcohol
  #  0.6, 2.1, # drugs
    13.1, 8.2, # BDI
    38.6, 36.4, # GSI
    46.5, 47.2, # SF-36 mental
    44.9, 46.5, # SF-36 social functioning
  #  2.9, 3.0, # Health rating
    45.5, 47.6, # SF-36 physical health
    
    # High propensity
  #  0.4, 1.0, # alcohol
  #  0.4, 1.3, # drugs
    10.0, 16.5, # BDI
    39.0, 42.3, # GSI
    49.5, 41.6,  # SF-36 mental
    47.5, 46.3, # SF-36 social functioning
  #  3.1, 4.3, # Health rating
    42.8, 48.8 # SF-36 physical health
  ),
  
  sd_post = c(
    # Low/medium propensity 
  #  2.1, 1.8, # alcohol
  #  1.9, 4.7, # drugs
    11.9, 6.6, # BDI
    9.2, 10.1, # GSI
    14.2, 13.7, # SF-36 mental
    12.6, 11.5, # SF-36 social functioning
  #  1.2, 1.0, # Health rating
    7.3, 10.1,  # SF-36 physical health
    
    # High propensity
  #  1.1, 1.4, # alcohol# drugs
  #  0.9, 1.9, # drugs
    10.9, 8.4, # BDI
    7.7, 5.9, # GSI
    11.1, 13.2,  # SF-36 mental
    12.0, 7.7, # SF-36 social functioning
  #  1.0, 0.5, # Health rating
    12.9, 6.6 # SF-36 physical health
  )
  
); Sacks2011

# Aggregate pretest measure

Sacks2011_prescores <- 
  Sacks2011 |> 
  select(-c(m_post:sd_post)) |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
  ) |>
  metafor::escalc(
    measure = "SMD", 
    m1i=m_pre_t, m2i=m_pre_c, 
    sd1i=sd_pre_t, sd2i=sd_pre_c, 
    n1i = N_t, n2i = N_c, 
    data = _
  ) 

Sack_pre_pooled <- 
  metafor::aggregate.escalc(Sacks2011_prescores, cluster = outcome, struct = "ID", addk = TRUE) |> 
  select(-c(yi:vi)) |> 
  mutate(sample = "low/medium propensity and high propensity sample")

# Aggregate posttest measure

Sacks2011_postscores <- 
  Sacks2011 |> 
  select(-c(m_pre:sd_pre)) |> 
  mutate(group = case_match(group, "Intervention" ~ "t", "Control" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N:last_col()
  ) |>
  metafor::escalc(
    measure = "SMD", 
    m1i=m_post_t, m2i=m_post_c, 
    sd1i=sd_post_t, sd2i=sd_post_c, 
    n1i = N_t, n2i = N_c, 
    data = _
  ) 

Sack_post_pooled <- 
  metafor::aggregate.escalc(Sacks2011_postscores, cluster = outcome, struct = "ID", addk = TRUE) |> 
  select(-c(yi:vi)) |> 
  mutate(sample = "low/medium propensity and high propensity sample")

Sack2011_pooled <- left_join(Sack_pre_pooled, Sack_post_pooled, by = join_by(outcome, sample, N_t, N_c, ki))


Sacks2011_est <- 
  Sack2011_pooled |> 
  mutate(

   analysis_plan = case_when(
     outcome == "BDI_total" ~ "All mental health outcomes/Depression",
      outcome == "GSI_total" ~ "All mental health outcomes",
      str_detect(outcome, "SF-36") ~ "Wellbeing and Quality of Life"),

   study = "Sacks et al. 2011",
  
   effect_size = "SMD",
   sd_used = "Pooled posttest SD",
   main_es_method = "Raw diff-in-diffs",
  
   # ppcor imputed
   ppcor = ppcor_imp,
   ppcor_method = "Imputed"


) |> 
  arrange(sample, outcome)|> 
  mutate(
    N_start_t = N_t,
    N_start_c = N_c,
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # For GSI and BDI lower scores are beneficial why these are reverted
    m_post = if_else(!outcome %in% c("BDI_total", "GSI_total"), m_post_t - m_post_c,
                     (m_post_t - m_post_c)*-1),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # For GSI and BDI lower scores are beneficial why these are reverted
    m_diff_t = if_else(!outcome %in% c("BDI_total", "GSI_total"), m_post_t - m_pre_t, (m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(!outcome %in% c("BDI_total", "GSI_total"), m_post_c - m_pre_c,(m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |>
  mutate(
         # Group sizes are imputed
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = paste0(outcome, "/", sample)
    
  ) |> 
  ungroup(); Sacks2011_est




```

```{r, echo=FALSE}

dat <- 
  dat |> 
  bind_rows(Sacks2011_est)

```

### **Sajatovic et al. [-@Sajatovic2009] (Quality-checked)**

Data was extracted from Table 2 (p. 1186)

```{r Sajatovic et al 2009}
# Data from Table 2 (p. 1186) provides mean and standard deviation. Table also 
# indcludes p-values indicating "Test of significance between the two groups 
# using repeated-measures analysis with PROC MIXED " (Sajatovic et al. 2009: 1186)


Sajatovic2009  <- tibble(
  group = rep(c("Life Goals Program", # Intervention
                "Treatment as usual"), # Control
              each = 1,9),
  
  outcome = rep(c("HAM-D", # Hamilton Depression Rating Scale
                   "YMRS", # Young Mania Rating Scale
                   "GAS"), # Global Assessment Scale
                 each = 2,3),
  
  timing = rep(c("3m", "6m", "12m"), each = 6),
  
    N_start = rep(c(
    83, 80,
    84, 80,
    83, 78
  ), each = 1, 3),
  
  N = c(
        # 3 months
        63, 65,
        63, 65,
        61, 61,
        
        # 6 months
        51, 55,
        51, 55,
        46, 53,
        
        # 12 months 
        41, 39, 
        41, 39, 
        40, 39
        ),
  
  
  
  m_pre = rep(c(
    19.98, 17.08, # HAM-D
    7.3, 7.58,    # YMRS
    56.53, 58.22  # GAS
    
  ), each =  1,3),
  
  
  sd_pre = rep(c(
    11.45, 10.99,
    5.41, 5.44,
    12.43, 12.00
    
  ), each = 1, 3),
  
  
  m_post = c(
    # 3 months
    16.30, 15.85,
    6.14, 8.02,
    60.10, 59.05,
    
    # 6 months
    16.35, 15.96,
    6.78, 7.69,
    61.72, 62.19, 
    
    # 12 months
    16.02, 14.39,
    5.85, 7.15,
    63.70, 64.51
  ), 
  
  
  sd_post = c(
    # 3 months
    9.68, 10.52,
    4.85, 5.38,
    11.63, 12.44, 
    
    # 6 months
    10.18, 12.47,
    5.36, 6.26,
    12.76, 14.42,
    
    # 12 months
    11.73, 10.87,
    4.74, 5.60,
    12.66, 15.90
  )
  
)


# Making the sajatovic2009 tibble wide in order to estimate the effect sizes and
# further analysis. 

# Turning data into wide format
    
Sajatovic2009_wide <- 
  Sajatovic2009 |>
  mutate(group = case_match(group, "Life Goals Program" ~ "t", "Treatment as usual" ~ "c")) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  )



Sajatovic2009_est <- 
  Sajatovic2009_wide |> 
  mutate(
    # P-values obtained from table 2, calculated with repeated-measures analysis with 
    # PROC MIXED in SAS (p. 1186) - 
    # p_val = rep(c(0.40, 0.22, 0.97), each = 1,3), # Made across time measurements 
    
    analysis_plan = rep(
      c("All mental health outcomes/Depression", # HAM-D
        "All mental health outcomes", # YMRS
        "Social functioning (degree of impairment)" # GAS
      ), 3),
    
    study = "Sajatovic et al. 2009", 
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    
  ) |> 
  mutate(timing = factor(timing, levels = c("3m", "6m", "12m"))) |> 
 # arrange(outcome, timing)|> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # For YMRS and HAM-D lower scores are beneficial why these are reverted
    m_post = if_else(outcome != "GAS", (m_post_t - m_post_c)*-1,
                     m_post_t - m_post_c),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For YMRS and HAM-D lower scores are beneficial why these are reverted
    m_diff_t = if_else(outcome != "GAS", 
                       (m_post_t - m_pre_t)*-1,
                       m_post_t - m_pre_t),
    m_diff_c = if_else(outcome != "GAS",
                       (m_post_c - m_pre_c)*-1,
                       m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # "Group size was generally six to eight members" (p. 1183)
    avg_cl_size = 7, 
    avg_cl_type = "From study (p. 1183)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ), 
    
    vary_id = paste0(outcome, "/", timing)
    
  ) |> 
  ungroup(); Sajatovic2009_est
```

```{r, echo=FALSE}
# Adding Sajatovic to data
dat <- 
  dat |> 
  bind_rows(Sajatovic2009_est)
```

### **Saloheimo et al. [-@Saloheimo2016] (Quality-checked)**

Sample size data was extracted from page 5. Effect size data was retrieved from Table 1 and text reported results (p. 6)

```{r}
Saloheimo2016_est <- 
  tibble(
    
    study = "Saloheimo 2016",
    outcome = "HAM-D",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    timing = "3m",
    
    N_start_t = 42,
    N_start_c = 46,
    
    N_t = 27,
    N_c = 34,
    
    N_total = N_t + N_c, 
    # I cant find these results anywhere (JKJ)
    m_pre_t = 19.3, 
    m_pre_c = 18.9,
    
    sd_pre_t = 3.8,
    sd_pre_c = 3.7,
    
    m_post_t = 8.3, 
    m_post_c = 11.1,
    
    sd_post_t = 6.3,
    sd_post_c = 6.4
    
    
  ) |> 
  mutate(
    
    analysis_plan = "All mental health outcomes/Depression",
    
    # ppcor imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed",
    
    df_ind = N_total,
    
    #  HAM-D lower scores are beneficial, thus these are reverted
    m_post = (m_post_t - m_post_c)*-1,
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For YMRS and HAM-D lower scores are beneficial why these are reverted
    m_diff_t =  (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate( # Attempt to make cluster corrected estimates. Has to be checked!
    
    # "Therapists act as coaches for groups of 4-6 patients" (p. 3)
    avg_cl_size = 3.47, 
    avg_cl_type = "From study (p. 4)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ), 
    
    vary_id = paste0(outcome, " (one es only)")
    
  ) |> 
  ungroup(); Saloheimo2016_est

```

```{r, echo=FALSE}
dat <- 
  dat |> 
  bind_rows(Saloheimo2016_est)
```

### **Schrank et al. [-@Schrank2016] (Quality-checked)**

Data was extracted from Tables 1, 2, and 3 in the same article (p. 240-242)

```{r Schrank et al 2016}
# Based on table 1, 2 and 3 in the same article 
Schrank2016 <- 
  tibble(
    group = rep(c("Intervention", "Control"), each = 1, 6),
    
    outcome = rep(c(
      "WEMWBS", 
      "MANSA", 
      # "PPI", 
      "BPRS",
      #   "SDHS",
      "IHS",
      "RSES", 
      "RES"
    ), each = 2,1),
    
    N_start = 47,
    
    N = rep(c(
      43, 41
    ), each = 1, 6),
    
    m_pre = c(
      3.19, 3.00,
      4.05, 4.14,
      #3.58, 3.44,
      30.70, 33.57,
      #2.29, 2.48,
      4.02, 3.72, 
      2.24, 2.09,
      2.74, 2.71
      ),
    
    sd_pre = c(
      0.76, 0.89,
      0.85, 1.01,
      #  0.73, 0.80,
      8.81, 8.42,
      #  0.69, 0.76,
      0.79, 0.85,
      0.64, 0.66,
      0.32, 0.32
    ),
    
    m_post = c(
      3.36,3.24,
      4.42, 4.21,
      # 3.72, 3.48,
      29.37, 33.23,
      #  2.13, 2.34,
      4.11, 4.04,
      2.37, 2.21,
      2.8,2.73
    ),
    
    se_post = c(
      0.10, 0.10,
      0.10, 0.10,
      #  0.07, 0.07,
      0.96, 0.98,
      #  0.08, 0.09,
      0.1, 0.1,
      0.07, 0.07,
      0.04,0.04) 
  ) |> 
  mutate(sd_post = se_post * sqrt(N)) # Calculating the standard deviation from the standard error




Schrank2016_wide <-
  Schrank2016 |> 
  mutate (group = case_match(
    group, "Intervention"  ~ "t", "Control" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  )



Schrank2016_est <-           
  Schrank2016_wide |>
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "WEMWBS|MANSA") ~ "Wellbeing and Quality of Life",
      str_detect(outcome, "BPRS") ~ "All mental health outcomes",
      str_detect(outcome, "IHS|RSES|RES") ~ "Hope, Empowerment & Self-efficacy",
      .default = NA_character_
    ),
    
    # ANCOVA results from table 3, p. 242
    F_val = c(
      0.8,
      2.3,
      #  5.9,
      7.8,
      #   3.0,
      3.0,
      2.9,
      2.0
    ),
    
    df1 = rep(c(1)),
    df2 = rep(c(81)),
    
    p_val_f = c(
      0.15,
      0.21,
      #    0.30,
      0.42,
      #    0.29,
      0.08,
      0.23,
      0.22
    )
    
  ) |> 
  mutate(
    study = "Schrank et al. 2016",
    
   effect_size = "SMD",
   sd_used = "Pooled posttest SD",
   main_es_method = "Raw diff-in-diffs",
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    ppcor_method = "Not computable - F value used for variance estimation",
    
    # The outcome BPRS is reverted because lower score is beneficial
    m_post = if_else(outcome != "BPRS", 
                     m_post_t - m_post_c,
                     (m_post_t - m_post_c) * -1),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # The outcome BPRS is reverted because lower score is beneficial
    m_diff_t = if_else(outcome != "BPRS", 
                       m_post_t - m_pre_t,
                       (m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(outcome != "BPRS",
                       m_post_c - m_pre_c,
                       (m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = d_DD^2/F_val + d_DD^2/(2*df_ind),
    Wd_DD = d_DD^2/F_val,
    
    g_DD = J * d_DD, 
    vg_DD = g_DD^2/F_val + g_DD^2/(2*df_ind),
    Wg_DD = g_DD^2/F_val
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # "WELLFOCUS PPT was provided to six groups, and each group had an average of eight" (p. 238)
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 238)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = grp_size_imp),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      F_val = F_val,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ),
    
    vary_id = outcome
  ) |> 
  ungroup(); Schrank2016_est
```

```{r, echo=FALSE}
# Adding Schrank to data
dat <- 
  dat |> 
  bind_rows(Schrank2016_est) 
```

### **Schäfer et al. [-@Schäfer2019] (Quality-checked)**

Entering data from table 2 (p. 9)

```{r Schäfer et al 2019}

# Taken from table 2, p. 9 
schafer2019 <- tibble(
  group = as.factor(rep(c("Seeking Safety",
                          "Relapse Prevention Training",
                          "TAU"),
                        each = 1, 21)),
  
  outcome = (rep(c("PSS-I", 
                   "PDS", 
                   "Drug and alcohol free days",
                   "ASI alcohol severity",
                   "ASI drug severity",
                   "BDI-II",
                   "DERS"),
                 each = 3,3)),
  
  timing = rep(c("Post", "3m", "6m"), each = 21),
  
  N_start = rep(c(111, 115, 117), each = 1, 21),

  N = rep(c(111, 115, 117), each = 1, 21),
  
  m_pre = rep(c(
    25.4, 27.5, 28.9,
    25.7, 27.6, 27.7,
    16.6, 15.6, 15.6,
    .28, .33, .32,
    .09, .1, .09,
    25.3, 29.4, 28.6,
    100.5, 110.7, 111.9),
    each = 1,3), 
  
  sd_pre = rep(c(
    9.7, 9.8, 9.4,
    11.2, 10., 10.2,
    12.1, 11.9, 12.3,
    .25, .29, .27,
    .11, .14, .11,
    13.2, 11.7, 10.9,
    26.9, 24.6, 25.3),
    each = 1,3), 
  
  
  
  m_post = c(
    # post mean values
    
    22.9, 24.3, 26.1,
    20.8, 23., 24.,
    18.3, 19.4, 16.3,
    .22, .25, .3,
    .07, .06, .08,
    19., 26.2, 25.8,
    95.6, 102.9, 109.1, 
    
    
    # 3m follow-up
    22.1, 23.7, 24.5,
    20.9, 22.5, 24.3,
    19.5, 21.1, 17.6,
    0.2, 0.25, 0.28,
    .06, .06, .06,
    19.9, 25., 25.2,
    94.1, 100.9, 106.8,
    
    #6m follow-up
    22.1, 20.7, 24.3,
    19.4, 19.9, 23.7,
    20.5, 22.4, 16.4,
    .24, .19, .27,
    .05, .04, .07,
    18.5, 22.3, 24.1,
    92.7, 100., 107.3),
  
  sd_post = c(
    # post sd values
    12.4, 11.9, 10.3,
    12.0, 11.2, 10.7,
    11.9, 11.7, 12.4,
    .24, .24, .28,
    .12, .11, .11,
    12.4, 12., 12.6,
    24.7, 26.4, 24.5,
    
    # 3m sd values
    12.2, 11.5, 11.8,
    13.8, 11.5, 12.5,
    11.6, 10.2, 11.9,
    .23, .26, .28,
    .11, .12, .10,
    14.4, 14., 13.0,
    27.2, 27.8, 26.1,
    
    # 6m sd values
    11.5, 11., 11.4,
    11.9, 11.7, 12.5,
    11.3, 10.7, 12.7,
    .26, .22, .28,
    .10, .10, .11,
    12.5, 13.3, 14.0,
    24.7, 25.2, 25.4
  )
)


# Effect sizes can be seen in table 2, p. 9
schafer2019_Relapse_wide <- 
  schafer2019 |> 
  filter(!str_detect(group, "Seeking")) |> 
  mutate(group = case_match(group, "Relapse Prevention Training" ~ "t", "TAU" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  ) |> 
  mutate(
    treatment = "Relapse Prevention Training"
  ) |> 
  relocate(treatment)

schafer2019_Seeking_wide <- 
  schafer2019 |> 
  filter(!str_detect(group, "Relapse")) |> 
  mutate(group = case_match(group, "Seeking Safety" ~ "t", "TAU" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  ) |> 
  mutate(
    treatment = "Seeking Safety"
  ) |> 
  relocate(treatment)



schafer2019_est <- 
  bind_rows(schafer2019_Relapse_wide, schafer2019_Seeking_wide) |> 
  arrange(outcome, timing) |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "ASI|Drug and alcohol free days") ~ "Alcohol and drug abuse/misuse",
      str_detect(outcome, "BDI-II") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "DERS") ~ "Unused outcomes",
      str_detect(outcome, "PDS|PSS-I") ~ "All mental health outcomes",
      .default = NA_character_
    ),
    
    study = "Schäfer et al. 2019",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor is imputed
    ppcor = ppcor_imp,
    ppcor_method = "Imputed"
    ) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    m_post = (m_post_t - m_post_c) * -1, 
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For all scores: Lower is beneficial
    m_diff_t = (m_post_t - m_pre_t)*-1,
    m_diff_c = (m_post_c - m_pre_c)*-1,
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # Group size was bup to 8 (p. 4)
    
    avg_cl_size = 8, 
    avg_cl_type = "From study (p. 4)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      omega * ((m_diff_t - m_diff_c)/BDI_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*BDI_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "BDI"),
      BDI_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
      
    vary_id = paste(outcome, timing, treatment, sep = "/")
  ) |> 
  ungroup(); schafer2019_est
```

```{r, echo=FALSE}
# Adding Schäfer to data
dat <- 
  dat |> 
  bind_rows(schafer2019_est)
```

### **Somers et al. [-@Somers2017] (Quality-checked)**

Data extracted from Table 1 (p. 7) and Table 3 (p. 9)

```{r}

Somers2017_est <- 
  tibble(
    
    study = "Somers et al. 2017",
    outcome = c(
      "Physical CIS",
      "Psychological CIS",
      "CSI",
      "GAIN-SPS",
      "QOL20"
    ),
    
    analysis_plan = c(
      "Social functioning (degree of impairment)",
      "Social functioning (degree of impairment)",
      "All mental health outcomes",
      "Alcohol and drug abuse/misuse",
      "Wellbeing and Quality of Life"
    ),
    analysis = "ITT",
    
    effect_size = "SMD",
    sd_used = "Pooled posttest SDs",
    
    main_es_method = "Raw diff-in-diffs",
    
    
    N_start_t = 90,
    N_start_c = 100,
    
    N_t = N_start_t,
    N_c = N_start_c,
    
    N_total = N_t + N_c, 
    df_ind = N_total,
    
    m_pre_t = c(2.1, 10.61, 37.12, 2.39, 72.61), 
    m_pre_c = c(1.83, 11.1, 40.25, 2.29, 74.72),
    
    sd_pre_t = c(1.75, 3.68, 12.91, 1.94, 21.69),
    sd_pre_c = c(1.70, 3.19, 12.49, 1.92, 21.43),
    
    m_post_t = c(2.82, 14.66, 26.25, 1.34, 91.80), 
    m_post_c = c(2.07, 12.62, 27.7, 1, 87.80),
    
    sd_post_t = c(1.90, 3.70, 10.98, 1.67, 24.55),
    sd_post_c = c(1.79, 3.70, 11.80, 1.57, 22.71),
    
    m_diff_t = c(0.72, 4.05, -10.87, -1.05, 19.19),
    cil_m_diff_t = c(0.32, 3.15, -13.42, -1.51, 14.34),
    ciu_m_diff_t = c(1.11, 4.94, -8.32, -0.59, 24.05),
    
    sd_diff_t = sqrt(N_t) * (ciu_m_diff_t - cil_m_diff_t)/3.92,
    
    m_diff_c = c(0.24, 1.52, -12.55, -1.29, 13.09),
    cil_m_diff_c = c(-0.15, 0.63, -15.31, -1.71, 8.01),
    ciu_m_diff_c = c(0.64, 2.40, -9.78, -0.88, 18.16),
    
    sd_diff_c = sqrt(N_c) * (ciu_m_diff_c - cil_m_diff_c)/3.92,
    
    
  ) |> 
  mutate(
    
    r_t = (sd_pre_t^2 + sd_post_t^2 - sd_diff_t^2)/(2*sd_pre_t*sd_post_t),
    
    z_t = 0.5 * log( (1+r_t)/(1-r_t) ),
    v_t = 1/(N_t-3),
    w_t = 1/v_t,
    
    r_c = (sd_pre_c^2 + sd_post_c^2 - sd_diff_c^2)/(2*sd_pre_c*sd_post_c),
    
    z_c = 0.5 * log( (1+r_c)/(1-r_c) ),
    v_c = 1/(N_c-3),
    w_c = 1/v_c,
    
    mean_z =  (w_t*z_t + w_c*z_c)/(w_t + w_c),  
    ppcor = (exp(2*mean_z)-1)/(exp(2*mean_z)+1),
    ppcor_method = "Calculated from study results",
   
    m_post = if_else(str_detect(outcome, "CIS|QOL"), (m_post_t - m_post_c), (m_post_t - m_post_c)*-1),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),    
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For YMRS and HAM-D lower scores are beneficial why these are reverted
    m_diff_t = if_else(str_detect(outcome, "CIS|QOL"), (m_post_t - m_pre_t), (m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(str_detect(outcome, "CIS|QOL"), (m_post_c - m_pre_c), (m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate( 
    
    # Could not find any estimate
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD
    ), 
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Somers2017_est


```

```{r, echo= FALSE}

dat <- # Not included in the covariaters sheet
  dat |> 
  bind_rows(Somers2017_est)

```

### **Tjaden et al. [-@Tjaden2021] (Quality-checked)**

Entering data from Table 2 (p. 1313)

```{r Tjaden et al 2021}
Tjaden2021 <- tibble(
  group = rep(c("Ressource group + FACT", #Ressource group
                "FACT"),                  #Control group
  each = 1, 8),
  

  outcome = rep(c("NEL",     # Netherlands Empowerment List
                  "BSI",     # Brief Symptom Inventory
                  "MANSA",   # Manchester Short assesment of Quality of Life
                  "GAF"),    # Global Assesment of functioning 
  each = 2, 2), 
 
  
  timing = rep(c("9m", "18m"), each = 8), 
  
   N_start = rep(c(
    80, 78,
    80, 78,
    80, 78,
    80, 78), 
  each = 1, 2),
  
  N = c(
    # 9 months
    70, 67,
    70, 67,
    70, 67,
    70, 67,
    
    # 18 months
    63, 58,
    63, 58,
    63, 58,
    63, 58),
  
   
  m_pre = rep(c(
    3.32, 3.34,
    2.11, 2.15,
    4.12, 4.26,
    47.91, 51.45),
    each = 1,2), 
  
  
  sd_pre = rep(c(
    0.51, 0.52,
    0.72, 0.85,
    0.88, 0.85,
    10.22, 10.58), 
    each = 1,2), 
  
  m_post = c(
    # 9 months
    3.55, 3.34,
    1.98, 1.98,
    4.49, 4.34,
    53.8, 54.03,
    
    # 18 months
    3.77, 3.38,
    1.92, 1.91,
    4.67, 4.48,
    58.33, 54.84),
  
  sd_post = c(
    # 9 months
    0.53, 0.62,
    0.86, 0.81,
    0.74, 0.90,
    10.16, 11.26,
    
    # 18 months
    0.57, 0.70,
    0.82, 0.76,
    0.74, 1.04,
    11.76, 13.43)
  
)

# Turning data into wide format


Tjaden2021_wide <- Tjaden2021 |> 
      mutate(group = case_match(group, "Ressource group + FACT" ~ "t", "FACT" ~ "c")) |>
      tidyr::pivot_wider(
        names_from = group,
        names_glue = "{.value}_{group}",
        values_from = N_start:last_col()
      ) #|> 
 # arrange(outcome, desc(timing))

Tjaden2021_est <- Tjaden2021_wide |> 
  # Based on the degrees of freedom value reported in Druss et al. 2018 table 2 and 3
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "BSI") ~ "All mental health outcomes",
      str_detect(outcome, "GAF") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "MANSA") ~ "Wellbeing and Quality of Life",
      str_detect(outcome, "NEL") ~ "Hope, Empowerment & Self-efficacy",
      .default = NA_character_
    ),
    
  # Effect sizes are acorss different time    
  #  d = c(
  #    rep(c(NA_real_), each = 4,1),
  #    c(0.54,
  #       0.25,
  #      0.07,
  #       0.30)),
    
  study = "Tjaden et al. 2021",
  
  effect_size = "SMD",
  sd_used = "Pooled posttest SD",
  main_es_method = "Raw diff-in-diffs",
  
  
  # ppcor imputed
  ppcor = ppcor_imp, 
  ppcor_method = "Imputed"
  
  )|> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    # For PANSS: reverted
    m_post = if_else(outcome %in% c("BSI"), 
                     (m_post_t - m_post_c)*-1,
                     m_post_t - m_post_c),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    # For all scores: Lower is beneficial
    m_diff_t = if_else(outcome %in% c("BSI"), (m_post_t - m_pre_t)*-1, m_post_t - m_post_c),
    m_diff_c = if_else(outcome %in% c("BSI"), (m_post_c - m_pre_c)*-1,m_post_c - m_pre_c),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # Group size is imputed
    
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      omega * ((m_diff_t - m_diff_c)/GAF_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*GAF_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "GAF"),
      GAF_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
   vary_id = paste0(outcome, "/", timing)
   ) |> 
   ungroup(); Tjaden2021_est



```

```{r, echo=FALSE}

dat <- 
  dat |> 
  bind_rows(Tjaden2021_est)

```

### **Valiente et al. [-@Valiente2022] (Quality-checked)**

Entering data from Table 3 and 4 (p. 4)

```{r Valiente et al 2022}
# Data from Table 3 and Table  4 (p. 4)  containing means, standard deviation. It also contains
# difference Morris' d and p

Valiente2022  <- tibble(
  group = rep(c("Multicomponent PPI", # Intervention
                "Treatment as usual"), # Control
              each = 1,16),
  
  outcome = rep(c(
    # Table 3
    "SPWB Autonomy",          #Scales of Psychological Well-Being (SPWB)
    "SPWB Positive_relationship", 
    "SPWB Self_acceptance",
    "SPWB Enviormental mastery",
    "SPWB Purpose_in_life",
    "SPWB Personal_Growth",
    "SWLS",                  # Satisfaction With Life Scale (SWLS)
    
    # Table 4
    "Somatization",         # Symptom checklist-90-Revised (SCL-90-R)
    "Obsessive–compulsive", 
    "Interpersonal sensibility",
    "Depression", 
    "Anxiety", 
    "Hostility", 
    "Phobic anxiety",
    "Paranoid ideation", 
    "Psychoticism"
    
  ), 
  each = 2,1),
  
    
  N_start = rep(c(71, 70), n_distinct(outcome)),
  
  N = rep(c(52,61),
          each = 1,16),
  
  m_pre = c(
    
    # Table 3
    19.5, 20.1,
    34.7, 33.9,
    29.9, 31.6,
    30.6, 31.4,
    34.4, 34.4,
    35.3, 35.7,
    17.4, 18.2,
    
    # Table 4
    1.00, 1.02,
    1.66, 1.62,
    1.42, 1.41,
    1.51, 1.47,
    1.12, 1.31,
    0.75, 0.63,
    0.89, 1.09,
    1.15, 1.15,
    1.14, 0.99
    
  ),
  
  sd_pre = c(
    # Table 3
    4.4,  4.2,
    8.0, 7.5,
    8.7, 7.8,
    7.8, 7.1,
    7.7, 6.6,
    7.4, 7.2,
    7.1, 6.8,
    
    # Table 4
    0.83, 0.81,
    0.87, 0.93,
    0.81, 0.95,
    0.87, 0.89,
    0.82, 0.97,
    0.83, 0.80,
    0.77, 1.01,
    0.86, 0.86,
    0.82, 0.83
    
  ),
  
  
  m_post = c(
    # Table 3
    19.9, 19.7,
    35.7, 34.4,
    31.7, 31.1,
    32.7, 30.8,
    34.9, 34.2,
    35.5, 35.5,
    19.0, 19.2,
    
    # Table 4
    0.90, 0.96,
    1.50, 1.48,
    1.28, 1.27,
    1.30, 1.40,
    1.07, 1.14,
    0.71, 0.61,
    0.91, 1.07,
    1.18, 1.21,
    0.99, 0.98
  ),
  
  sd_post = c(
    # Table 3
    3.4, 4.2, 
    7.1, 7.6, 
    7.5, 7.8, 
    7.5, 7.3, 
    6.9, 5.9,
    6.8, 6.8,
    6.6, 6.8,
    
    # Table 4
    0.82, 0.84,
    0.86, 0.96,
    0.79, 0.89,
    0.80, 0.94,
    0.84, 0.91,
    0.76, 0.79,
    0.80, 0.97,
    0.90, 0.87,
    0.85, 0.86
  )
  
)

# Making the tibble wide 

Valiente2022_wide <-
  Valiente2022 |> 
  mutate (group = case_match(
    group, "Multicomponent PPI"  ~ "t", "Treatment as usual" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  )

# Effect sizes: Estimating G and D (pre-post test) (Not finished yest)
Valiente2022_est <-           
  Valiente2022_wide |>
  mutate(
    analysis_plan = rep(c(
      "Wellbeing and Quality of Life",
      "Wellbeing and Quality of Life",
      "All mental health outcomes"
    ), c(6,1,9)),
    
    study = "Valiente et al. 2022",
    
  effect_size = "SMD",
   sd_used = "Pooled posttest SD",
   main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor = ppcor_imp, 
    ppcor_method = "Imputed"
    
    
  )|> 
  mutate(
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    # The outcome SCL-90-R reverted because lower score is beneficial
    m_post = if_else(analysis_plan != "All mental health outcomes", m_post_t - m_post_c,
                     (m_post_t - m_post_c) * -1),
    
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For BSI: Lower is beneficial
    m_diff_t = if_else(analysis_plan != "All mental health outcomes", m_post_t - m_post_c, (m_post_t - m_pre_t)*-1),
    m_diff_c = if_else(analysis_plan != "All mental health outcomes", m_post_c - m_pre_c, (m_post_c - m_pre_c)*-1),
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
    
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # Group size is imputed
    
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Valiente2022_est
```

```{r, echo=FALSE}
# Adding Valiente to data
dat <- 
  dat |> 
  bind_rows(Valiente2022_est) 
```

### **Volpe et al. [-@Volpe2015] (Quality-checked)**

Entering data from Table 2 (p. 19)

```{r Volpe et al 2015}
Volpe_2015 <- tibble(
  # Taken from table 2, p. 19
  outcome = rep(c(
    "BPRS",   # Brief Psychiatric Rating Scale
    "PHQ-9",  # Patient Health Questionnaire
    "PSP",    # Personal and Social Performance Scale
    "DAS-II"  # Disability Assessment Schedule 2.0
  ), each = 2, 1), 
  
  
  group = rep(c(
    "Reading group",
    "Control group"
  ), each = 1, 4), 
  
  N_start = rep(c(25)),

  N = rep(c(21,20), each = 1, 4), 

  m_pre = c(
    49.95, 51.2, # Brief Psychiatric Rating Scale
    9.74, 9.9,   # Patient Health Questionnaire
    44.09, 46.2, # Personal and Social Performance Scale
    2.64, 2.95   # Disability Assessment Schedule 2.0
  ),
  
  sd_pre =c(
    15.48, 17.75,
    4.58, 4.35,
    16.19, 18.23,
    0.93, 0.94
  ),
  
  m_post = c(
    36.21, 41.2,
    4.7, 5.66,
    49.95, 47.25,
    1.25, 2.13
  ), 
  
  sd_post = c(
    17.43, 15.69,
    2.78, 2.45,
    15.53, 18.35,
    0.5, 0.25
  )
)



# Making the tibble into wideformat
Volpe2015_est <-
  Volpe_2015 |> 
  mutate(group = case_match(group, "Reading group" ~ "t", "Control group" ~ "c")) |> 
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from = N_start:last_col()
  ) |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "BPRS") ~ "All mental health outcomes",
      str_detect(outcome, "PHQ-9") ~ "All mental health outcomes/Depression",
      str_detect(outcome, "PSP|DAS-II") ~ "Social functioning (degree of impairment)"),
    
    
    study = "Volpe et al. 2015",
    effect_size = "SMD",
    sd_used = "Pooled posttest SD",
    main_es_method = "Raw diff-in-diffs",
    
    
    # ppcor imputed
    ppcor = ppcor_imp, 
    ppcor_method = "Imputed",
    
    
    # MANOVA estimates obtained from raw text on page 18 for between-group comparisons. 
    # But the effect-sizes seems wrong when using them. 
    #  F_val = c(NA_real_, 
    #            NA_real_,
    #            7.29,
    #            3.07),
    
    #  df1 = c(NA_real_,
    #          NA_real_,
    #          1, 
    #          1),
    
    #  df2 = c(NA_real_,
    #          NA_real_,
    #          39,
    #          39),
    
    #  p_val_F = c(NA_real_,
    #              NA_real_,
    #              0.008,
    #              0.005
    #  ),
    
    N_total = N_t + N_c,
    
    N_total = N_t + N_c,
    df_ind = N_total,
    
    
    # For BPRS, PHQ-9, and DAs-II lower scores are beneficial why these is reverted
    m_post = if_else(outcome != "PSP", (m_post_t - m_post_c)*-1, m_post_t - m_post_c),
    sd_pool = sqrt(((N_t-1)*sd_post_t^2 + (N_c-1)*sd_post_c^2)/(N_t + N_c - 2)),  
    
    d_post = m_post/sd_pool, 
    
    vd_post = (1/N_t + 1/N_c) + d_post^2/(2*df_ind),
    Wd_post = (1/N_t + 1/N_c),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_post = J * d_post,
    vg_post = (1/N_t + 1/N_c) + g_post^2/(2*df_ind),
    Wg_post = Wd_post,
    
    
    # For BPRS, PHQ-9, and DAs-II lower scores are beneficial why these is reverted
    m_diff_t = if_else(outcome != "PSP", (m_post_t - m_pre_t)*-1, m_post_t - m_post_c),
    m_diff_c = if_else(outcome != "PSP", (m_post_c - m_pre_c)*-1, m_post_c - m_pre_c), 
    
    
    d_DD = (m_diff_t - m_diff_c)/sd_pool,
    vd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + d_DD^2/(2*df_ind),
    Wd_DD = 2*(1-ppcor) * (1/N_t + 1/N_c),
    
    g_DD = J * d_DD, 
    vg_DD = 2*(1-ppcor) * (1/N_t + 1/N_c) + g_DD^2/(2*df_ind),
    Wg_DD = Wd_DD
  ) |> 
  rowwise() |> 
  mutate(
    # Average cluster size in treatment group
    # Group size is imputed
    
    avg_cl_size = grp_size_imp, 
    avg_cl_type = "Imputed",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    n_covariates = 1,
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
    ),
    
    # Calculated via Eq. 7 (Hedges & Citkowicz, 2015)
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size),
    omega = 1 - 3/(4*df_adj-1),
    
    
    # Cluster-adjusting g_post
    gt_post = omega * d_post * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_post, 
      model = "posttest",
      cluster_adj = FALSE,
      add_name_to_vars = "_post",
      vars = c(vgt_post, Wgt_post)
    ),
    
    gt_DD = omega * d_DD * gamma_sqrt,
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_DD, 
      model = "DiD",
      cluster_adj = FALSE,
      prepost_cor = ppcor,
      q = n_covariates,
      add_name_to_vars = "_DD",
      vars = -var_term1_DD),
    
    gt_DD_pop = if_else(
      str_detect(outcome, "9"),
      omega * ((m_diff_t - m_diff_c)/PHQ_pop_res$sd_population) * gamma_sqrt,
      NA_real_
      ),
  
    # Calculated from Eq. 28 (Fitzgerald & Tipton, 2024)
    vgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD) + gt_DD_pop^2/(2*PHQ_pop_res$df_pop),
      NA_real_
      ),
    
    Wgt_DD_pop = if_else(
      str_detect(outcome, "9"),
      PHQ_pop_res$WaboveT * (2*(1-ppcor) * (1/N_t + 1/N_c) * adj_value_DD),
      NA_real_
      ),
    
    vary_id = outcome
    
  ) |> 
  ungroup(); Volpe2015_est
```

```{r, echo=FALSE}
# Adding Volpe to data
dat <- 
  dat |> 
  bind_rows(Volpe2015_est)
```

### **Wojtalik et al. [-@Wojtalik2019] (Quality-checked)**

Entering data from Table 2 and 3 (p. 505)

```{r}
# Data from table 2 and table (p. 505)  containing marginal means with standard error
# It also contains standard error. To calculate pre-post test we will use the standard 
# deviation from table 1 (p. 504)

Wojtalik2019 <- tibble( 
  group = as.factor(rep(c("Cognitive enhancement therapy (CET)", # Intervention
                          "Enriched supportive therapy (EST)"), # Control
                        each = 1,4)),
  
  analysis = rep(c("ITT", "Completers"), each = 4),
  
  
 # timing = 18,
  
  outcome = as.factor(rep(c(
    #      "Cognition composite",
    "Social adjustment composite",
    "Symptoms composite"), 
    each = 2,2)),
  
   N_start = rep(c(
    58, 44), each = 1,4),
 
  N = c(rep(c(58, 44), each = 1,2),
        rep(c(26, 23),each = 1,2)),
  
  
  b_emm_pre = c(
    # ITT
    #    34.4, 38,  # Baseline Cognition composite 
    49.5, 51.0,# Baseline Social adjustment composite
    50.2, 50.4, # Baseline Symptoms composite
    
    
    # Completers
    
    #    34.6, 39.4, # Baseline Cognition composite
    47.1, 50.4, # Baseline Social adjustment composite
    49.4, 50.2 # Baseline Symptoms composite
    
  ),
  
  # Taken from table 1 (p. 504)
  sd_pre = rep(
    c(
      # ITT
      #    11.3, 11.8, # Baseline Cognition composite  
      9.6, 10.5,  # Baseline Social adjustment composite
      10.4, 10.0), 2
  
    # Baseline Symptoms composite
  
  # Completer (taken from online supplements)
  #rep(c(
  #  #    12.52, 12.52,
  #  8.80, 8.80,
  #  9.18, 9.18
  #), each = 1,1)
  ),
  
  se_emm_pre = rep(c(
    # ITT
    #    1.5, 1.7,  # Baseline Cognition composite 
    1.3, 1.4,  # Baseline Social adjustment composite
    1.3, 1.5,   # Baseline Symptoms composite
    
    # Completer
    #    2.4, 2.6,
    1.8, 1.8,
    1.9, 2.0
    
  ), each = 1,1),
  
  b_emm_post = c(
    # ITT
    #    38.4, 37.1, # 9 months Cognition composite
    #    54.1, 56.8, # 9 months Social adjustment composite
    #    53.5, 53.4, # 9 months Symptoms composite
    
    #    40.1, 39.8, # 18 months Cognition composite
    56.3, 55.5, # 18 months Social adjustment composite
    54.3, 53.5,  # 18 months Symptoms composite
    
    # Completer
    #    40.0, 37.5, # 9 months Cognition composite
    #    52.2, 54.9, # 9 months Social adjustment composite
    #    53.2, 52.8, # 9 months Symptoms composite
    
    #    41.3, 40.6, # 18 months Cognition composite
    54.5, 53.4, # 18 months Social adjustment composite
    54.0, 53.1  # 18 months Symptoms composite
    
  ),
  
  se_emm_post = c(
    # ITT
    #    1.6, 1.9, # 9 months Cognition composite
    #    1.4, 1.7, # 9 months Social adjustment composite
    #    1.4, 1.7, # 9 months Symptoms composite
    
    #    1.8, 2.0, # 18 months Cognition composite
    2.0, 2.3, # 18 months Social adjustment composite
    1.8, 1.9,  # 18 months Symptoms composite
    
    # Completer
    #    2.4, 2.6, # 9 months Cognition composite
    #    1.9, 2.0, # 9 months Social adjustment composite
    #    1.8, 1.9, # 9 months Symptoms composite
    
    #    2.5, 2.6, # 18 months Cognition composite
    2.4, 2.5, # 18 months Social adjustment composite
    2.0, 2.1  # 18 months Symptoms composite
    
  )
  
); Wojtalik2019 

# Making the tibble into wide format
Wojtalik2019_wide <- Wojtalik2019 |> 
  mutate(
    group = case_match(
      group, 
      "Cognitive enhancement therapy (CET)" ~ "t", 
      "Enriched supportive therapy (EST)" ~ "c"
    )
  ) |>
  tidyr::pivot_wider(
    names_from = group,
    names_glue = "{.value}_{group}",
    values_from =  N_start:last_col()
  ) |> 
  mutate(
    t_val = c(0.8, 0.43, 1.60, 0.60),
    es_paper = c(0.23, 0.11, 0.51, 0.18),
    es_paper_type = "Glass' delta (assumed)"
  
  ); Wojtalik2019_wide

Wojtalik2019_est <- 
  Wojtalik2019_wide |> 
  mutate(
    analysis_plan = case_when(
      str_detect(outcome, "Social adjustment composite") ~ "Social functioning (degree of impairment)",
      str_detect(outcome, "Symptoms composite") ~ "All mental health outcomes"),
    
    study = "Wojtalik et al. 2019", 
    
    effect_size = "SMD",
    sd_used = "Pooled pretest SD - Posttest SD not reported",
    main_es_method = "Raw diff-in-diffs",
    
    # ppcor imputed
    ppcor_method = "Not computable - t values used"
    
  ) |> 
  mutate(
    N_total = N_t + N_c,
    df_ind = N_total,
    
    mean_diff = b_emm_post_t - b_emm_post_c,
    sd_pool = sqrt(((N_t-1)*sd_pre_t^2 + (N_c-1)*sd_pre_c^2)/(N_t + N_c - 2)),
      
    d_adj = mean_diff/sd_pool,
    vd_adj = d_adj^2/t_val^2 + d_adj^2/(2*df_ind),
    Wd_adj = d_adj^2/t_val^2,
    
    J = 1 - 3/(4*df_ind-1),
    
    g_adj = J * d_adj,
    vd_adj = g_adj^2/t_val^2 + g_adj^2/(2*df_ind),
    Wg_adj = g_adj^2/t_val^2
    
    
  ) |> 
  rowwise() |> 
  mutate(
    avg_cl_size = 4, 
    avg_cl_type = "From study (p. 530)", 
    
    icc = ICC_01, 
    icc_type = "Imputed", 
    n_covariates = 1, 
    
    gamma_sqrt = gamma_1armcluster(N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc), 
    
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size), 
    
    omega = 1 - 3 / (4 * df_adj - 1), 
    
    gt_adj = omega * d_adj * gamma_sqrt, 
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_adj, 
      model = "emmeans", 
      cluster_adj = FALSE, 
      t_val = t_val, 
      q = n_covariates, 
      add_name_to_vars = "_adj", 
      vars = -c("var_term1_adj")
      ),
    
    vary_id = paste0(outcome, "/", analysis)
  ) |>  
  ungroup(); Wojtalik2019_est
```

```{r, echo=FALSE}
# Adding Wojtalik to data
dat <- 
  dat |> 
  bind_rows(Wojtalik2019_est) 
```

### **Wuthrich & Rapee [-@Wuthrich2013] and Smith et al. [-@Smith2021] (Quality-checked)**

Entering data from Table 2 (p. 445). Sample size information is retrieved from Table 1 (p. 442).

```{r Wuthrich20132021dat}
# Calculate paired t-test statistics to obtain pre-posttest score correlation estimates

#-----------------------------------------------------------------
# OUTCOME ANCRONYMS AND FULL NAMES
#-----------------------------------------------------------------
# GDS = Geriatric Depression Scale
# CES-D = The Centre for Epidemiological Studies Depression Scale (loneliness only item used)
# GAI = Geriatric Anxiety Inventory
# PSWQ = The Penn State Worry Questionnaire
# SF12 = The Short Form 12 version 1 Mental Health Subscale
#-----------------------------------------------------------------


# F-stats from the interaction between time and group for mean
# It was uncertain whether we could use these estimates. Therefore, we didn't use
# the reported F-statistics. 
F_stat_Wuthrich <- 
  tibble(
    
    year = c(13, 21, 21, 13, 21, 13, 13, 13),
    
    outcome = c(
      "Primary problem", 
      "Anxiety", 
      "Depression", 
      "GDS", 
      "Loneliness (CES-D 14)", 
      "GAI", 
      "PSWQ", 
      "SF12"),
    
    df1 = 1,
    df2 = c(
      
      47.95,  # Primary problem severity (2013, p. 783)
      49.603, # Anxiety disorder severity (2021, p. 444)
      45.451, # Depressive disorder severity [Mood] (2021, p. 444)
      50.88,  # GDS (2013, p. 783)
      40.609, # Loneliness (2021, p. 444)
      55.65,  # GAI (2013, p. 783)
      54.95,  # PSWQ (2013, p. 783)
      51.39   # SF15 (2013, p. 783)
      
      ),
    F_val = c(
      
      17.36, 
      17.858,
      30.884,
      8.86,
      7.070,
      7.4,
      0.57,
      qf(.988, df1 = 1, df2 = df2[8], lower.tail = FALSE)
      
    ),
    
    pval_reported = c(
      
      pf(F_val[1], df1 = 1, df2 = df2[1], lower.tail = FALSE),
      pf(F_val[2], df1 = 1, df2 = df2[2], lower.tail = FALSE),
      pf(F_val[3], df1 = 1, df2 = df2[3], lower.tail = FALSE),
      .004,
      .011,
      .009,
      .452,
      .988
      
    )
  
)

F_stat_Wuthrich

## emm = estimated marginal means

Wuthrich2013_2021_smd <- 
  tibble(
    
    outcome = rep(
      c("Primary problem", 
        "Anxiety", 
        "Depression", 
        "GDS", 
        "Loneliness (CES-D 14)",
        "GAI",
        "PSWQ",
        "SF12"
        ), 
      each = 2),
    
    group = rep(c("CBT", "Waitlist"), n_distinct(outcome)),
    
    N_start = rep(c(27, 35), n_distinct(outcome)), # From Table 2, 2013, p. 783
    
    N = rep(c(27, 35), n_distinct(outcome)), # From Table 2, 2013, p. 783
    
    b_emm_pre = c(
      
      6.33, 5.81,    # ADIS primary disorder severity (from Table 3, 2013, p. 784)
      5.2, 4.58,     # ADIS mean anxiety disorder severity (from Table 2, 2021, p. 445)
      5.48, 4.98,    # ADIS depressive disorder severity (from Table 2, 2021, p. 445)
      18.14, 16.35,  # Geriatric Depression Scale (from Table 3, 2013, p. 784)
      1.59, 1.36,    # Loneliness (CES-D item 14) (from Table 2, 2021, p. 445)
      11.59, 9.48,   # Geriatric Anxiety Inventory (from Table 3, 2013, p. 784)
      53.62, 49.27,  # Penn State Worry Questionnaire (from Table 3, 2013, p. 784) 
      37.09, 38.45   # Short Form-12 (Mental) (from Table 3, 2013, p. 784)
      
    ),
    
    b_emm_post = c(
      
      3.46, 5.15,   # ADIS primary disorder severity (from Table 3, 2013, p. 784)
      2.68, 4.01,   # ADIS mean anxiety disorder severity (from Table 2, 2021, p. 445)
      2.02, 4.25,   # ADIS depressive disorder severity (from Table 2, 2021, p. 445)
      9.21, 14.38,  # Geriatric Depression Scale (from Table 3, 2013, p. 784)
      .593, 1.24,   # Loneliness (CES-D item 14) (from Table 2, 2021, p. 445)
      5.84, 8.33,   # Geriatric Anxiety Inventory (from Table 3, 2013, p. 784)
      46.54, 47.24, # Penn State Worry Questionnaire (from Table 3, 2013, p. 784)
      47.79, 43.56  # Short Form-12 (Mental) (from Table 3, 2013, p. 784)
      
    ),
    
    se_emm_post = c(
      
      0.29, 0.24,
      0.307, .227,
      .319, .260,
      1.44, 1.23,
      0.169, 0.194,
      1.20, 1.04,
      2.80, 2.41,
      2.26, 1.97
      
    ),
    
    sd_pre = c(
      
      0.653, 1.16,  # ADIS primary disorder severity (from Table 2, 2013, p. 783)
      0.74, 1.07,   # ADIS mean anxiety disorder severity (from Table 1, 2021, p. 442)
      1.05, 1.27,   # ADIS depressive disorder severity (from Table 1, 2021, p. 442)
      6.18, 5.99,   # Geriatric Depression Scale (from Table 2, 2013, p. 783)
      0.82, 0.97,   # Loneliness (CES-D item 14)  (from Table 1, 2021, p. 442)
      4.72, 5.43,   # Geriatric Anxiety Inventory (from Table 2, 2013, p. 783)
      12.39, 13.06, # Penn State Worry Questionnaire (from Table 2, 2013, p. 783)
      9.22, 6.69    # Short Form-12 (Mental) (from Table 2, 2013, p. 783)
      
    ),
    
    # Number of controlled covariates
    q = rep(c(2, 4, 4, 2, 4, rep(2, 3)), each = 2)
    
  )

Wuthrich2013_2021_smd

Wuthrich2013_2021_wide <- 
  Wuthrich2013_2021_smd |>
  mutate(group = case_match(group, "CBT" ~ "t", "Waitlist" ~ "c")) |> 
  pivot_wider(
    names_from = group,
    values_from = N_start:sd_pre
  ) |> 
  rename(n_covariates = q)

#Wuthrich2013_2021_wide
```

Calculating effect sizes and conducting a small number of clusters correction, as suggested by WWC [-@WWC_clusterbias].

```{r Wuthrich20132021SMDcalc}

Wuthrich2013_2021_es_smd <- 
  Wuthrich2013_2021_wide |> 
  mutate(
    
    effect_size = "SMD",
    sd_used = "Pooled pretest SD",
    
    study = "Wuthrich et al.",
    main_es_method = "Posttest emmeans",
    
    
    R2 = R2_imp,
    R2_method = "Imputed",
    
    N_total = N_t + N_c,
    
    df_ind = N_total,
    
    # Reverting outcomes so all outcomes has the same direction
    b_emm = if_else(unique(outcome) != "SF12", (b_emm_post_t - b_emm_post_c) * -1, b_emm_post_t - b_emm_post_c),
    sd_pool = sqrt(((N_t-1)*sd_pre_t^2 + (N_c-1)*sd_pre_c^2)/(N_t + N_c - 2)), 
    
    d_adj = b_emm/sd_pool,
    vd_adj = (1/N_t + 1/N_c) * (1-R2) + d_adj^2/(2*df_ind),
    Wd_adj = (1/N_t + 1/N_c) * (1-R2),
    
    J = 1 - 3/(4*df_ind-1),
    
    g_adj= J * d_adj,
    vg_adj = (1/N_t + 1/N_c) * (1-R2) + g_adj^2/(2*df_ind),
    Wg_adj = (1/N_t + 1/N_c) * (1-R2),

    
  ) |> 
  rowwise() |> 
  mutate(
    
    # Average cluster size in treatment group
    # "in blocks of 8 participants." (2013, p. 781)
    avg_cl_size = 8, 
    avg_cl_type = "From study (2013, p. 781)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    # Find info about the function via ?VIVECampbell::gamma_1armcluster
    gamma_sqrt = VIVECampbell::gamma_1armcluster(
      N_total = N_total, Nc = N_c, avg_grp_size = avg_cl_size, ICC = icc
      ),
    
    df_adj = df_h_1armcluster(N_total = N_total, ICC = icc, N_grp = N_t, avg_grp_size = avg_cl_size), 
    omega = 1 - 3 / (4 * df_adj - 1), 
    
    gt_adj = omega * d_adj * gamma_sqrt, 
    VIVECampbell::vgt_smd_1armcluster(
      N_cl_grp = N_t, 
      N_ind_grp = N_c, 
      avg_grp_size = avg_cl_size, 
      ICC = icc, 
      g = gt_adj, 
      model = "emmeans", 
      cluster_adj = FALSE, 
      R2 = R2, 
      q = n_covariates, 
      add_name_to_vars = "_adj", 
      vars = -c("var_term1_adj")
      )
  
  ) |> 
  ungroup()



```

Calculating odds ratio effect sizes from Recovery rates reported in @Wuthrich2013 [p.784]

```{r Wuthrich20132021ORcalc}

# Odd ratio based on recovery rates reported in Wuthrich 2013 (p. 784)

Wuthrich2013_OR_dat <- 
  tibble(
    study = "Wuthrich et al.",
    outcome = "Recovery rates",
    effect_size = "OR",
    main_es_method = "Proportions",
    
    N_start_t = 27,
    N_start_c = 35,
    
    N_t = 20,
    N_c = 26,
    p_t = 0.53, 
    p_c = 0.11,
    
    # Average cluster size in treatment group
    # "in blocks of 8 participants." (2013, p. 781)
    avg_cl_size = 8, 
    avg_cl_type = "From study (2013, p. 781)",
    
    # Imputed icc value
    icc = ICC_01,
    icc_type = "Imputed",
    
    # OR calculation and cluster bias adjustment
    VIVECampbell::OR_calc(
      p1 = p_t, p2 = p_c, n1 = N_t, n2 = N_c, 
      ICC = icc, avg_cl_size = avg_cl_size, n_cluster_arms = 1
    )
    
  )

Wuthrich2013_OR_dat


# Amalgamating the SMD and OR results
Wuthrich2013_2021_est <- 
  bind_rows(Wuthrich2013_2021_es_smd, Wuthrich2013_OR_dat) |> 
  mutate(
    study = "Wuthrich et al. 2013/2021",
    vary_id = outcome,
    analysis_plan = case_when(
           outcome =="Primary problem" ~ "All mental health outcomes/Anxiety", 
           outcome == "Anxiety" ~ "All mental health outcomes/Anxiety", 
           outcome == "Depression" ~ "All mental health outcomes/Depression", 
           outcome == "GDS" ~ "All mental health outcomes/Depression", 
           outcome == "Loneliness (CES-D 14)"~"Loneliness" ,
           outcome == "GAI"~"All mental health outcomes/Anxiety" ,
           outcome == "PSWQ"~"Unused outcomes",
           outcome == "SF12"~"Wellbeing and Quality of Life",
           outcome == "Recovery rates" ~ "Unused outcomes"  # Couldn't figure this one out (Jakob)
         )
  )


Wuthrich2013_2021_est

#rm(Wuthrich2013_2021_smd_est, Wuthrich2013_OR_dat)
```

```{r, echo=FALSE}
# Adding Wuthrich to data

dat <- 
  dat |> 
  bind_rows(Wuthrich2013_2021_est)

```

## **Amalgamating covariate and effect size data**

```{r dataamalgamation}
# REMEMBER TO REMOVE EMPOWERMENT OUTCOME FROM BARBIC2009

#Remember to see working directory to "Group-based-interventions/ES calc"

cov_string <- if (isTRUE(getOption('knitr.in.progress')))  {
  "Descriptive coding scheme for group-based interventions (data).xlsx"
  } else {
    "ES calc/Descriptive coding scheme for group-based interventions (data).xlsx"
  }

covariates <- read_xlsx(cov_string, na = c(".", ""))

group_based_dat <- 
  bind_cols(
    covariates,
    dat
)

# To check the symmetry between R-file and Excel-extraction sheet
#D <- group_based_dat |> 
#  select(Author, vary_id, varifier)

#write_xlsx(group_based_dat, "Group-based interventions data.xlsx")
#saveRDS(group_based_dat, file = "Group-based interventions data.RDS")
#save(group_based_dat, file = "Group-based interventions data.RData")
```

## **Using extant data to estimate population standard deviations**

To increase the generalization of the effect size estimates, we used extant data (i.e., all standard deviations from the control groups that has been measure on the same scale) to calculate population-based effect sizes. We only do so when more than four studies has reported the same outcomes on the same scale. This was recommended by Fitzgerald & Tipton [-@Fitzgerald2024]. We could calculate this standard deviation for the Global Assessment of Functioning Scale, Patient Health Questionnaire-9 (PHQ-9), and Beck Depression Inventory (BDI). 
\ 

Below, you find the function we used for these calculations--all of which can be found Fitzgerald & Tipton [-@Fitzgerald2024].
```{r}
# Function contain ANOVA estimation techniques from Fitzgerald & Tipton 2024
population_SMD <- 
  function(data){
  
  data |> 
  summarise(
    N = sum(N_total),
    N_0 = sum(N_c),
    J = n_distinct(study),
    sigma2_WA = sum((N_total-2)*sd_pool^2)/(N-2*J),
    m_post_mean_c = mean(m_post_c),
    SSB0 = sum(N_c*(m_post_c - m_post_mean_c)^2),
    MSB0 = SSB0/(J-1),
    n0_star = N_0 - (sum(N_c^2/N_0)/J-1),
    
    #Equation 12
    sigma2_B = (MSB0 - sigma2_WA)/n0_star,
    sigma2_TA = ((n0_star-1)/n0_star) * sigma2_WA + (MSB0/n0_star),
    sd_population = sqrt(sigma2_TA),
    n_star = (N_0 - sum(N_c^2/N_0))/(J-1),
    c1 = (n_star-1)*sigma2_WA/(n_star*(N-2*J)),
    c2 = (sigma2_WA + n_star * sigma2_B)/(n_star*(J-1)),
    v1 = N_0-2*J,
    v2 = J-1,
    # Eq. 29
    df_pop = (c1*v1 + c2*v2)^2/(c1^2*v1 + c2^2+v2),
    WaboveT = sigma2_WA/sigma2_TA
    
  )
    
}

```

### GAF population measures

```{r}

GAF_dat <- 
  dat |> 
  # Filtering GAF measures and remove Gonzalez and Prihoda since they don't
  # provide enough data to be used for this calculation
  filter(str_detect(outcome, "GAF") & !str_detect(study, "Gon")) |> 
  relocate(study) 

GAF_dat_restricted <- 
  GAF_dat |> 
  group_by(study) |> 
  filter(row_number() == 1) |> 
  ungroup()

ggplot(data.frame(x = c(0, 100)), aes(x)) + 
  mapply(function(mean, sd, col) {
    stat_function(fun = dnorm, args = list(mean = mean, sd = sd), linetype = "dashed", color = col)
  }, 
  # enter means, standard deviations and colors here
  mean = GAF_dat_restricted$m_post_c, 
  sd = GAF_dat_restricted$sd_post_c,
  col = "black"
  ) + 
  theme_bw() +
  labs(x = "GAF score", y = "Density")
  
GAF_res <- population_SMD(GAF_dat_restricted)
GAF_res

#saveRDS(GAF_res, "ES calc/GAF_res.rds")

```


### PHQ-9 population measures

```{r}

PHQ_9_dat <- 
  group_based_dat |>
  filter(
    str_detect(authors, "Cano|Hilden|Himle|Lloyd|Volpe") & 
    str_detect(outcome, "9") & measure_type == 	"Post-intervention"
  )

# Removing follow-up effect size from Cano-Vindel
PHQ_9_dat <- PHQ_9_dat[-2,]

ggplot(data.frame(x = c(0, 35)), aes(x)) + 
  mapply(function(mean, sd, col) {
    stat_function(fun = dnorm, args = list(mean = mean, sd = sd), linetype = "dashed", color = col)
  }, 
  # enter means, standard deviations and colors here
  mean = PHQ_9_dat$m_post_c, 
  sd = PHQ_9_dat$sd_post_c,
  col = "black"
  ) + 
  theme_bw() +
  labs(x = "PHQ-9 score", y = "Density")

PHQ_9_res <- population_SMD(PHQ_9_dat)
PHQ_9_res

#saveRDS(PHQ_9_res, "ES calc/PHQ_9_res.rds")

```

### BDI population measures

```{r}

BDI_dat <- 
  group_based_dat |>
  filter(
    str_detect(authors, "Craigie|Hagen|Jacob|Michalak|Rabenstein|Sacks|Schäfer") &
    str_detect(outcome, "BDI")
  )

BDI_dat <- BDI_dat[-c(2, 5, 7, 10:13, 15),]

ggplot(data.frame(x = c(0, 60)), aes(x)) + 
  mapply(function(mean, sd, col) {
    stat_function(fun = dnorm, args = list(mean = mean, sd = sd), linetype = "dashed", color = col)
  }, 
  # enter means, standard deviations and colors here
  mean = BDI_dat$m_post_c, 
  sd = BDI_dat$sd_post_c,
  col = "black"
  ) + 
  theme_bw() +
  labs(x = "BDI_dat score", y = "Density")

BDI_res <- population_SMD(BDI_dat)
BDI_res

#saveRDS(BDI_res, "ES calc/BDI_res.rds")

```

## **References**